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In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 μg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations. [Display omitted] • Assessed cropland use impact on groundwater arsenic (As) by RF and SHAP. • AUC of 0.86 for As hazards (>50 μg/L) prediction considering cropland use. • The importance of crop areas was as follows: sunflower > melon > wheat > maize. • Transitioning from sunflower to wheat or melon may mitigate As hazards. [ABSTRACT FROM AUTHOR]

Machine learning models are often viewed as black boxes in landslide susceptibility assessment, lacking an analysis of how input features predict outcomes. This makes it challenging to understand the mechanisms and key factors behind landslides. To enhance the interpretability of machine learning models in wide-area landslide susceptibility assessments, this study uses the Shapely method to explore the contributions of feature factors from local, global, and spatial perspectives. Landslide susceptibility assessments were conducted using random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, focusing on the geologically complex Sichuan–Tibet region. Initially, the study revealed the contributions of specific key feature factors to landslides from a local perspective. It then examines the overall impact of interactions among feature factors on landslide occurrence globally. Finally, it unveils the spatial distribution patterns of the contributions of various feature factors to landslide occurrence. The analysis indicates the following: (1) The XGBoost model excels in landslide susceptibility assessment, achieving accuracy, precision, recall, F1-score, and AUC values of 0.7815, 0.7858, 0.7962, 0.7910, and 0.86, respectively; (2) The Shapely method identifies the leading factors for landslides in the Sichuan–Tibet region as Elevation (3000–4000 m), PGA (1–2 g), NDVI (<0.5), and distance to rivers (<3 km); (3) Using the Shapely method, the study explains the contributions, interaction mechanisms, and spatial distribution patterns of landslide susceptibility feature factors across local, global, and spatial perspectives. These findings offer new avenues and methods for the in-depth exploration and scientific prediction of landslide risks. • Utilizing spatial distribution patterns of landslide susceptibility in Sichuan and Tibet using SHAP values. • Analysis of the effect of evaluation factors on landslide susceptibility was analyzed. • Explored the Shapley method's feasibility for assessing landslide susceptibility. • Explained the process of employing the XGBoost model for evaluating landslide susceptibility. [ABSTRACT FROM AUTHOR]

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan—under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041–2060 and 2081–2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region. • Machine learning algorithms were applied to predict flood susceptibility in South Asia. • The RRF model had the highest accuracy (AUC = 0.94) in flood susceptibility modeling. • CMIP6 data used to predict flood susceptibility under SSP1-2.6 and SSP5-8.5 scenarios. • Projected flood susceptibility maps for 2041–2060 and 2081–2100 periods under climate change impacts. [ABSTRACT FROM AUTHOR]

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010–2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development. • The supply and demand of three critical ecosystem services were evaluated. • The spatiotemporal supply-demand relationships of ecosystem services were identified. • The natural and human drivers of these relationship shifts were examined. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization have greatly contributed to boosting regional economic growth and mitigating the problem of poverty, but blind expansion of cities and towns has not only caused the inefficient use of urban land resources but also caused the deterioration in the urban ecological environment. Within the current context of emphasizing high-quality development, achieving synergy between the efficient use of urban land and ecological environmental protection is an urgent task for promoting new urbanization construction. In this study, cities in the upper reaches of the Yangtze River (URYR) were adopted as the research object, a theoretical analysis framework for the urban land use efficiency (ULUE) and ecological environment quality (EEQ) was established, the ULUE was measured by using the Slacks-Based Measure (SBM) model, the coupling coordination and interactive corresponding response relationship between the ULUE and EEQ were analyzed, and the influencing factors of the coupling coordination between these two systems were explored by using the random forest model. The following conclusions can be obtained: in 2020, compared with those in 2006, both the ULUE and EEQ were improved, and the two systems exhibited interactions and significant spatiotemporal heterogeneity. The coupling coordination degree (CCD) between the ULUE and EEQ could facilitate maintaining the original state, and the transfer of the CCD exhibited a significant spatial correlation with the state of neighbouring cities. The effect of the ULUE on the EEQ indicated nonlinear characteristics, while the effect of the EEQ on the ULUE was manifested as inhibition initially and then promotion. The random forest regression results showed that the population density, landscape agglomeration and connectivity, market conditions, government intervention, and industrial institutions are the key influencing factors of the CCD. Finally, this study provides policy implications for innovative urban land use modelling, environmental regulation, and industrial transformation and upgrading. • Put forward the framework of coupling coordination and interactive response between urban land use efficiency (ULUE) and ecological environment quality (EEQ) firstly. • Socio-economic and natural data were used to assess the ULUE. • The influencing factors of the coupling coordination between ULUE and EEQ were explored by using the random forest model. • Results can guide the implementation of land utilization and ecological protection. [ABSTRACT FROM AUTHOR]

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils. • The effectiveness of a variety of auxiliary variables was assessed in predicting EC and SAR. • PLS latent variables (LVs) derived from Vis-NIR led to the highest performance for predicting EC and SAR. • Among the various ML models, Random Forest (RF) demonstrated the highest efficiency. [ABSTRACT FROM AUTHOR]

This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved to be the most accurate model in terms of determination coefficient, mean squared error, and mean absolute error. The analysis revealed that the harvested area of permanent crops is the most influential variable in predicting deforestation, followed by the area of temporary crops. Furthermore, our findings indicate a significant inverse relationship between public spending and deforestation rates. These results contribute to understanding deforestation dynamics and offer potential strategies for improving conservation efforts. • We analyze deforestation in the Brazilian Amazon from 1999 to 2020. • We employ machine learning to assess 16 critical factors. • Random Forest was the most accurate of five machine learning models. • Harvested permanent crop area dominates deforestation predicted in most models, followed by temporary crop area. • Higher public spending was linked to reduced deforestation rates, suggesting conservation interventions. [ABSTRACT FROM AUTHOR]

Reliable nitrogen (N) fertilizer management indicators are essential for improving crop yields and minimizing environmental impacts for sustainable production. The objectives of this study were to assess the importance of major N management indicators (NMIs) for higher yield with low risks of environmental pollution in an intensive potato system under drip irrigation. Six drip-irrigated field experiments with no N application (Control), farmer practice (FP), and optimized N management (OM) based on N-balance, soil mineral N (N min), and target yield were conducted from 2018 to 2020 in Inner Mongolia, China. The response of NMIs to potato yield and yield-based environment impact indices (EI Y) was evaluated by the random forest algorithm. The N input, N losses from N leaching, ammonia (NH 3) volatilization, nitrous oxide (N 2 O) emission, N use efficiency (NUE), N surplus, and soil residual N after harvest were obtained to identify the best NMIs for high yield and minimal ecological impact. The N management practices in field experimental sites affected the importance of the order of NMIs on potato yield and EI Y. The NUE and N leaching were identified as the highest importance scores and the most essential controlling variables to potato yield and EI Y , respectively. The integrated NUE and N leaching indicator played a vital role in improving potato yield and reducing ecological impact. The OM treatment achieved 46.0%, 63.6%, and 64.6% lower in N application rate, N surplus, and reactive N loss, and 62.4% higher in NUE than the FP treatment while achieving equal potato yields, respectively. Those key NMIs can guide farmers in understanding their practice short comes to achieve both high productivity and environmental sustainability in intensive potato production systems under drip irrigation. • We assess the response of nitrogen (N) indicators to drip-irrigated potato system. • Yield and yield-base environment indices (EI Y) were affected by NUE and leaching. • The N surplus was an effective indicator for controlling N losses. [ABSTRACT FROM AUTHOR]

In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensively assess the ESG performance of the solid waste treatment industry and identify company types, this study constructs a targeted ESG evaluation index system based on existing literature, SASB industry standards, and company reports and utilizes a random forest approach combined with K-means clustering to determine indicator weights. Based on this index system, the paper evaluates the ESG performance of 71 solid waste disposal companies (SWDCs) from 2013 to 2021 and identifies their ESG types from static and dynamic perspectives. In the static view, company types are determined based on annual ESG performance, while the dynamic view considers time-series changes to observe the evolution of company ESG types. The results show that the overall ESG performance of SWDCs falls within the 2-8-point range, indicating a noticeable high-low imbalance. Key initiatives to improve ESG performance in this industry include enhancing waste management measures, developing emergency plans, and reinforcing ESG disclosure. From a static perspective, this paper can identify companies into three categories: delayed development, single-wheel-driven, and coordinated development. Finally, from a dynamic perspective considering the time factor, companies are further subdivided into five types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia. This study not only provides targeted recommendations for different types of ESG companies but also helps various sectors of society better understand the ESG conditions of this high environmental risk industry, thereby enhancing the regulation and support for its sustainable development. [Display omitted] • ESG evaluation index system for solid waste disposal companies is proposed. • A random forest method based on k-means clustering is used to construct the index. • Comparing E, S, and G scores with industry averages to identify static ESG types. • Analyzing trends in annual static ESG types to identify dynamic ESG types. • Identify companies as three static ESG types and five dynamic ESG types. [ABSTRACT FROM AUTHOR]

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability. • Study the impact of green finance and green innovation on corporate credit rating. • Use Chinese A-share listed firms from 2018 to 2021 and machine learning techniques. • A 1% increase in green finance diminishes credit rating by 0.26%. • Impact is more pronounced in heavily polluting firms and non-state-owned firms. • Green innovation promotes credit rating performance by 0.15%. [ABSTRACT FROM AUTHOR]

Chlorophyll fluorescence is the long-wave light released by the residual energy absorbed by vegetation after photosynthesis and dissipation, which can directly and non-destructively reflect the photosynthetic state of plants from the perspective of the mechanism of photosynthetic process. Moso bamboo has a substantial carbon sequestration ability, and leaf-expansion stage is an important phenological period for carbon sequestration. Gross primary production (GPP) is a key parameter reflecting vegetation carbon sequestration process. However, the ability of chlorophyll fluorescence in moso bamboo to explain GPP changes is unclear. The research area of this study is located in the bamboo forest near the flux station of Anji County, Zhejiang Province, where an observation tower is built to monitor the carbon flux and meteorological change of bamboo forest. The chlorophyll fluorescence physiological parameters (Fp) and fluorescence yield (Fy) indices were measured and calculated for the leaves of newborn moso bamboo (I Du bamboo) and the old leaves of 4- to 5-year-old moso bamboo (Ⅲ Du bamboo) during the leaf-expansion stage. The chlorophyll fluorescence in response to the environment and its effect on carbon flux were analyzed. The results showed that: F v /F m , Y (II) and α of Ⅰ Du bamboo gradually increased, while Ⅲ Du bamboo gradually decreased, and FY int and FY 687 /FY 738 of Ⅰ Du bamboo were higher than those of Ⅲ Du bamboo; moso bamboo was sensitive to changes in air temperature(Ta), relative humidity(RH), water vapor pressure(E), soil temperature(ST) and soil water content (SWC), the Fy indices of the upper, middle and lower layers were significantly correlated with Ta, E and ST; single or multiple vegetation indices were able to estimate the fluorescence yield indices well (all with R2 greater than 0.77); chlorophyll fluorescence (Fp and Fy indices) of Ⅰ Du bamboo and Ⅲ Du bamboo could explain 74.4% and 72.7% of the GPP variation, respectively; chlorophyll fluorescence and normalized differential vegetation index of the canopy (NDVIc) could estimate GPP well using random forest (Ⅰ Du bamboo: r = 0.929, RMSE = 0.069 g C·m−2; Ⅲ Du bamboo: r = 0.899, RMSE = 0.134 g C·m−2). The results of this study show that chlorophyll fluorescence can provide a basis for judging the response of moso bamboo to environmental changes and can well explain GPP. This study has important scientific significance for evaluating the potential mechanisms of growth, stress feedback and photosynthetic carbon sequestration of bamboo. • Over the course of leaf spreading, moso bamboo's photosynthetic capability varied. • Moso bamboo is sensitive to temperature and moisture during leaf-expansion stage. • Chlorophyll fluorescence can explain more than 70% of GPP variation of moso bamboo. • Chlorophyll fluorescence can be used to estimate GPP well using random forests. [ABSTRACT FROM AUTHOR]

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors. • Assess urban waterlogging susceptibility using three types of data-driven WSAMs. • Evaluate comprehensive performance of WSAMs using a complex evaluation index-DISO. • WELLSVM used unlabeled clusters shows high accuracy with limited flood inventories. • Use LR-based WSAM to reduce the uncertainty of susceptibility assessment. [ABSTRACT FROM AUTHOR]

Hydro-morphological threats impact the natural physical characteristics of river ecosystems, such as flow regimes, sediment transport, and channel morphology. These negative effects can occur at multiple scales, ranging from local microhabitats to geographic regions. Understanding these interactions can be useful for an integrated conservation approach and is needed for effective freshwater management. The aim of the study was to elucidate the combined effects of hydro-morphological threats on macroinvertebrates at three spatial scales: macroscale, including whole catchments, mesoscale (hydro-morphological changes in individual river sections) and the microscale, describing the microhabitat conditions of European rivers. The diversity and trophic structure of 1120 local macroinvertebrate communities in 28 catchments of various hydro-morphological disturbance levels, ranging from 0 to 2400 m asl, were analyzed. The response of macroinvertebrates to the main disturbance gradient differed between mountain and lowland communities. Random forest analysis indicated that the most important predictor of the ecological, diversity, and trophic indices was described by flow rate reduction. Generalized additive mixed models showed that decreased flow combined with river incision explained most of the variation in macroinvertebrate indices. Our results emphasize that based on multi-spatial scale analysis, hydro-morphological threats are very important factors in invertebrates biodiversity loss. Thus, to implement effective river management, we should pay more attention to the combined effects of geomorphological threats. • Hydro-morphological disturbances lead to the loss of invertebrate biodiversity. • The effects of hydro-morphological pressure were studied at various spatial scales. • Incision and flow reduction described most of the river macroinvertebrates variation. • The most favourable microhabitats for macrofauna are shallow and redeposited sites. • Efficient river management should focus on the combined effects of multiscale threats. [ABSTRACT FROM AUTHOR]

The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to estimate and predict reservoir sedimentation using multilayer perceptron–artificial neural network (MLP-ANN) and random forest regressor (RFR) models in the Gibe-III reservoir, Omo-Gibe River basin. The hydrological and meteorological parameters considered for the estimation and prediction of reservoir sedimentation include annual rainfall, annual water inflow, minimum reservoir level, and reservoir storage capacity. The MLP-ANN and RFR models were employed to estimate and predict the amount of sediment accumulated in the Gibe-III reservoir using time series data from 2014 to 2022. ANN-architecture N4-100-100-1 with a coefficient of determination (R2) of 0.97 for the (80, 20) train-test approach was chosen because it showed better performance both in training and testing (validation) the model. The MLP-ANN and RFR models' performance evaluation was conducted using MAE, MSE, RMSE, and R2. The models' evaluation result revealed that the MLP-ANN model outperformed the RFR model. Regarding the train data simulation of MLP-ANN and RFR shown R2 (0.99) and RMSE (0.77); and R2 (0.97) and RMSE (1.80), respectively. On the other hand, the test data simulation of MLP-ANN and RFR demonstrated R2 (0.98) and RMSE (1.32); and R2 (0.96) and RMSE (2.64), respectively. The MLP-ANN model simulation output indicates that the amount of sediment accumulation in the Gibe-III reservoir will increase in the future, reaching 110 MT in 2030–2031, 130 MT in 2050–2051, and above 137 MTin 2071–2072. [Display omitted] • The storage capacity is the most important parameter in reservoir sediment prediction. • Forecasted data increases uncertainties in artificial neural network model prediction. • The artificial neural network model showed better performance in sediment prediction. • Uncertainty quantification determines the performance of the artificial neural network model. • Remote sensing data enhances the performance of the artificial neural network model in reservoir sedimentation prediction. [ABSTRACT FROM AUTHOR]

Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes. • We used the transductive support vector machine (TSVM) for forest fire susceptibility assessment with limited samples. • The prediction accuracy of TSVM outperforms common supervised learning methods in small sample scenarios. • TSVM as a semi-supervised learning exploits unlabeled data to improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community. This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2 cv) of the training dataset was 0.71, whereas the R2 coefficient (R2 test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent. [Display omitted] • A meso-scale habitat model for the macroinvertebrate community is developed. • Flow velocity preferences are used to describe the community assemblage and distribution. • Random Forest is applied to assess the ecological response to flow variation. • The free online service SimStream-Web is available to perform calculations. • Meso-Scale habitat model (MesoHABSIM) is extended to the macroinvertebrate community. [ABSTRACT FROM AUTHOR]

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive demonstrably improves when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, 'C-Dax' reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed "SPECTRA-FOR" (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML. [Display omitted] • New method to optimise Sentinel-2 imagery using Planet Labs SuperDove data. • The near-infrared band is more sensitive for retrieving aboveground biomass. • The random forest algorithm can capture the grazing regimes of individual farms. • Model performance and evaluation rely on quality data and ample field observations. • The evaluation of the model with monthly data outperformed the varied temporal scales. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region. • Landscape changes and associated ecological risks were simulated in the Mongolian Plateau. • The intensity and extent of land use change in Inner Mongolia are greater than in Mongolia. • Land-use changes generally led to increased landscape ecological risk (LER). • The high-risk area was observed to increase exponentially with higher future RCP concentrations. • The SSP2-RCP4.5 pathway was identified as the most favorable scenario for promoting sustainable development. [ABSTRACT FROM AUTHOR]

By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development. • Eight BiSeNet model to classify basalt land uses/covers and landscape assessment were developed. • Performance of BiSeNet using input size of 128x128 with Nadam optimizer is highest. • Three different indices including DCAD, TECI, and SHDI were chosen to assess landscape quality. • Outcomes of BiSeNet models are used effectively to monitor different landscape indices. • Landscape quality in 41 geosites of Dak Nong UGGp was monitored over 30 years. [ABSTRACT FROM AUTHOR]

Flow obstructed by bridge piers can increase sediment transport leading to local scour. This local scour poses a risk to the stability of bridge structures, which could lead to structural failures. There are two main approaches for evaluating the scour depth (d s) of bridge piers. The first is based on understanding hydraulic phenomena and developing relationships with properties affecting scour. The second uses data-driven soft computing models that lack physical interpretations but rely on algorithms to predict outcomes. Methods are chosen by researchers based on their goals and resources. This study aims to create innovative ensemble frameworks comprising support vector machine for regression (SVMR), random forest regression (RFR), and reduced error pruning tree (REPTree) as base learners, alongside bagging regression tree (BRT) and stochastic gradient boosting (SGB) as meta learners. These ensembles were developed to analyse maximum scour depths (d sm) in clear water conditions, utilizing 35 literature's experimental data published in last 63 years. The performance of each machine learning (ML) approach was assessed using statistical performance indicators. The proposed model was also compared with top six empirical equations with strong predictive ability. Results show that among these empirical equations, the equation from Nandi and Das (2023) performs best. Performance evaluation considering training, testing, and the entire dataset, SGB (REPTree), BRT(SVMR-PUK), and SGB (REPTree) exhibited the highest performance, securing the top rank among all ML models and empirical equations. Sensitivity analysis identified sediment gradation and flow intensity as the most influential variables for predicting d sm during both training and testing phases, respectively. • Predict maximum clearwater scour (d sm), by metadata analysis of 850 datasets from 35 literatures. • Compare six most promising empirical equations alongwith novel ML techniques. • Among all models, SGB(REPTree), BRT(SVMR-PUK), and RFR perform best for training, testing and all datasets, respectively. • Sensitivity analysis confirms sediment gradation and flow intensity as the most influential parameters. • nalysis identifies SGB(RFR) model with lowest and Jain and Fischer (1979) equation with highest prediction uncertainty. [ABSTRACT FROM AUTHOR]

Data-driven machine learning approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden. But the performance and reliability are strongly influenced by different sources of uncertainty. Conventional researches generally rely on a stand-alone machine learning surrogate approach and fail to account for errors in model outputs resulting from structural deficiencies. To overcome this issue, this study proposes a flexible integrated Bayesian machine learning modeling (IBMLM) method to explicitly quantify uncertainties originating from structures and parameters of machine learning surrogate models. An Expectation-Maximization (EM) algorithm is combined with Bayesian model averaging (BMA) to find out maximum likelihood and construct posterior predictive distribution. Three machine learning approaches representing different model complexity are incorporated in the framework, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF). The proposed IBMLM method is demonstrated in a field-scale real-world "1500-foot" sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intrusion (SWI) issues. This study adds to the understanding of how chloride concentration transport responds to multi-dimensional extraction-injection remediation strategies in a sophisticated saltwater intrusion model. Results show that most IBMLM exhibit r values above 0.98 and NSE values above 0.93, both slightly higher than individual machine learning, confirming that the IBMLM is well established to provide better model predictions than individual machine learning models, while maintaining the advantage of high computing efficiency. The IBMLM is found useful to predict saltwater intrusion without running the physically based numerical simulation model. We conclude that an explicit consideration of machine learning model structure uncertainty along with parameters improves accuracy and reliability of predictions, and also corrects uncertainty bounds. The applicability of the IBMLM framework can be extended in regions where a physical hydrogeologic model is difficult to build due to lack of subsurface information. • An integrated Bayesian machine learning modeling method is newly developed. • Uncertainties from surrogate model structures and parameters are explicitly quantified. • Most IBMLM exhibit r values above 0.98 and NSE values above 0.93. • Saltwater intrusion response to aquifer remediation strategies is quickly predicted. • Extraction-injection along with a cutoff barrier works effectively to control SWI. [ABSTRACT FROM AUTHOR]

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017–2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4–51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future. [Display omitted] • Three machine learning algorithms were first used to build soil loss models. • Random forest demonstrated the most favorable performance for soil loss modeling. • Soil loss caused by rainstorms was 4.4–51.3 times that of other rainfall events. • Bare land was the predominant source of soil loss. [ABSTRACT FROM AUTHOR]

Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts. [Display omitted] • EDNA and remote sensing datasets are combined to predict species distribution. • Thirteen RS-based vegetation indices are closely related to eDNA-inferred richness. • Aquatic insects can achieve spatial distribution prediction within 500 m reach. [ABSTRACT FROM AUTHOR]

Ecological restoration projects in the Tibetan Plateau aimed to reverse ecosystem degradation and safeguard the fragile alpine ecological environment. However, it is still being determined if the vegetation restoration is successful on a large scale or reaches the expected magnitude, restricting our ability to adapt practices to maximise the benefit. With multiple vegetation indices (VIs: NDVI, LAI, and GPP) from satellite observations and random forest machine-learning models, we performed an attribution study on vegetation growth trends caused by climate change and human activities. Then, we further explored the relationship between vegetation growth and ecological projects and human footprint without the influence of climate. The results showed that climatic change was a relatively strong driver of vegetation growth. The positive contributions of ecological restoration occurred only in half of the plateau due to the increased human footprint. Vegetation enhancement resulting from ecological restoration occurred in 13.1%–23.1% of the plateau. Among these values, ecological restoration counteracted the negative climate effects in 4.7%–8.3% of the plateau (about half of the negative climate effect area). In forest and grassland protection areas, the ecological restoration was more successful. The study provides a better understanding of the role of ecological projects in vegetation restoration and potential threats to its effectiveness. This is essential to improve future restoration projects. • Climate change primarily driven vegetation growth trends in the Tibetan Plateau. • The positive contributions of ecological restoration occurred only in half of the plateau due to continued human pressures. • Vegetation enhancement resulting from ecological restoration occurred in 13.1%–23.1% of the plateau. • Ecological restoration prevented nearly half of the areas subjected to adverse climate forces from experiencing degradation. • In forest and grassland protection areas, the ecological restoration was more successful. [ABSTRACT FROM AUTHOR]

Old-growth forests provide a broad range of ecosystem services. However, due to poor knowledge of their spatiotemporal distribution, implementing conservation and restoration strategies is challenging. The goal of this study is to compare the predictive ability of socioecological factors and different sources of remotely sensed data that determine the spatiotemporal scales at which forest maturity attributes can be predicted. We evaluated various remotely sensed data that cover a broad range of spatial (from local to global) and temporal (from current to decades) extents, from Airborne Laser Scanning (ALS), aerial multispectral and stereo-imagery, Sentinel-1, Sentinel-2 and Landsat data. Using random forests, remotely sensed data were related to a forest maturity index available in 688 forest plots across four ranges of the French Alps. Each model also includes socioecological predictors related to topography, socioeconomy, pedology and climatology. We found that the different remotely sensed data provide information on the main forest structural characteristics as defined by ALS, except for Landsat, which has a too coarse resolution, and Sentinel-1, which responds differently to vegetation structure. The predictions were quite similar considering aerial remotely sensed data, on the one hand, and satellite remotely sensed data, on the other hand. Socioecological variables are the most important predictors compared to the remote sensing metrics. In conclusion, our results indicate that a wide range of remotely sensed data can be used to study old-growth forests beyond the use of ALS and despite different abilities to predict forest structure. Accounting for socioecological predictors is indispensable to avoid a significant loss of predictive accuracy. Remotely sensed data can allow for predictions to be made at different spatiotemporal resolutions and extents. This study paves the way to large-scale monitoring of forest maturity, as well as for retrospective analyses which will show to what extent predicted maturity change at different dates. • Old-growth forests provide a variety of ecosystem services. • Knowledge about their spatial and temporal distribution is very scarce. • We show how available remotely sensed data sources can fill this gap. • Our results pave the way to large-scale spatiotemporal predictions. [ABSTRACT FROM AUTHOR]

Urbanisation is a key aspect of land use change (LUC), and accurately modelling of urban LUC is crucial for sustainable development. Cellular automata (CA) are widely used in LUC research. However, previous studies have overlooked the significant temporal dependence and spatial heterogeneity associated with LUC. To address these gaps, this study proposes a novel model called KCLP-CA, which integrates k-means, a convolutional neural network (CNN), a long and short-term memory neural network (LSTM), and the popular patch-generation land use model (PLUS). Initially, k-means and CNN are utilised to address spatial heterogeneity, while LSTM tackles temporal dependence. The LSTM and land expansion analysis strategy (LEAS) models of PLUS are employed to obtain land use conversion probability maps. Finally, a simulation of land use dynamic change was conducted using a linear weighted fusion conversion probability map that accounts for random factors. To validate the KCLP-CA model, land use data collected from Hangzhou between 1995 and 2000 were employed. The results showed that the KCLP-CA model outperformed traditional methods, including artificial neural networks and random forest model, with the figure of merit (FoM) index increasing from 2.12% to 4.19%. Random forest analysis of drivers impacting LUC revealed that distance to water and road network density exerted the greatest influence on urban land development in Hangzhou. Incorporation of various policy planning factors affecting urban development yielded simulation results aligning more closely with reality, resulting in a FoM index increase of 1.64–1.76%. In summary, the model developed in this study combines the strengths of two sub models to deliver an accurate and effective simulation of future land use. [Display omitted] • Effects of spatiotemporal dependence on land use change were studied. • A novel KCLP-CA model was proposed in this study. • This model effectively combines the advantages of the KCL-CA and PLUS models. • Improved simulation outcomes were observed when the KCLP-CA model was employed. [ABSTRACT FROM AUTHOR]

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization. [Display omitted] • We developed a novel framework to map boro rice at peak season using Sentinel images. • Boro rice maps in Bangladesh showed high classification accuracy (mean of 87.90%). • No requirement of sample data collection for training the classification model. • Multi-Otsu effectively maps rice in low-data areas, outperforming other ML methods. • Provide stakeholders rice area statistics to support food security management. [ABSTRACT FROM AUTHOR]

The impact of the urban morphology on greenhouse gas emission is one of the key issues on global climate change. Since the urban form is directly related to the spatial distribution of urban land use, it is necessary to investigate the relation between carbon emission and different land use categories. In this paper, the city of Eindhoven (230,000 inhabitants) was used as a case study. According to the main road network, the entire city is divided into 6754 irregular patterns. Agglomerative cluster analysis was conducted to classify the patterns into 14 valid land use categories based on their land use function and land cover composition namely: agriculture, transport, retail trade, green space (with 3 sub-categories), residential (with 7 sub-categories), and others. The random forest algorithm was applied to select the significant features and to measure the relation between land use and carbon emission. The results have shown the importance of various landscape metrics on the carbon emission in each land use category. The most significant landscape metric is selected to describe the impact of spatial attributes on carbon emission. The outcomes show the carbon emission distribution of each land use category in the city. The retail trade and residential land use categories contribute a large proportion of carbon emission, terrace houses produce more carbon emission than other residential building categories. The combination of mid-rise buildings and low-rise buildings has a higher probability to produce more carbon emission. The assessment results can provide important support for the low carbon city spatial planning. • Statistics of landscape metrics shows high reliability on land use classification. • Random forest and hierarchical clustering improve classification performance. • CO 2 emission in the residential area is significantly influenced by building layout. • Knowledge of the relation of land use and CO 2 emission supports fine scale plan. [ABSTRACT FROM AUTHOR]

There are growing concerns about increases in the size, frequency, and destructiveness of wildfire events. One commonly used mitigation strategy is the creation and maintenance of defensible space, a zone around buildings where vegetation is managed to increase potential for structures to survive during wildfires. Despite widespread acceptance and advocacy of defensible space, few studies provide empirical evidence documenting the efficacy of different fuel modification practices under real wildfire conditions. The 2018 Woolsey Fire in Los Angeles County, California, occurred a short time after high-resolution (0.07 m2) land cover data were created, providing a unique opportunity to quantify vegetation before the fire. We integrated measurements from this high-resolution land cover data with parcel data, building attributes, and environmental context. We then used Random Forests models to analyze the extent to which these factors predicted structure loss in the wildfire. Variable importance scores showed vegetation around buildings was not a strong predictor of building-level damage outcomes compared to building materials and landscape features such as paved land cover per parcel, elevation, building density, and distance to road networks. Among building materials, multi-paned windows and enclosed eaves were most highly associated with building survival. These results are consistent with other studies that conclude building materials and environmental context are more related to survivorship than defensible space. • We analyzed structure loss after the 2018 Woolsey Fire with Random Forest models. • High resolution land cover (0.07 m2) provides new measures of defensible space. • Building materials and landscape level features were the best predictors of loss. • Vegetation immediately around buildings was less related to survival. • Findings emphasize the importance of enhancing building materials to reduce losses. [ABSTRACT FROM AUTHOR]

Environmental crimes are a global issue due to the damage they cause to landscapes and ecosystems. This study focused on characterizing environmental crimes in the Canary Islands (Spain). Four categories of environmental crimes related to construction, mining and tilling, solid waste, and liquid waste) were defined and analysed. A total of 28 databases were generated, corresponding to each of the 7 major islands and each environmental crime typology. Each database was linked to information on land use and the socioeconomic and physical characteristics of the territory. For each database, firstly a descriptive statistical analysis was conducted, followed by the generation of a regularized Random Forest model with the aim of identifying characteristics that may be related to the location of environmental crimes. The results showed that, in most cases, proximity to residential accommodations, agricultural areas and industrial zones act as the main explanatory features of the distribution of environmental crimes. Furthermore, a marked pattern of concentration of environmental crimes in the coastal belt of the islands was observed, mainly associated with urban-tourist development since the 1960s and 1970s. [Display omitted] • Study reveals environmental crime (EC) patterns in Canary Islands. • Random Forest model was used to detect environmental relationships with EC occurrence. • Proximity to residential, agricultural, industrial zones linked to EC. • Coastal areas major hotspots due to urban-tourist development. • Call for improved environmental crime quantification methods. [ABSTRACT FROM AUTHOR]

Sewerage and stormwater networks are subjected to several deterioration factors, including aging, environmental conditions, and traffic. Maintaining these critical assets in good condition is essential to avoid harmful consequences, such as environmental contamination and negative implications on other infrastructure systems (e.g., water and road networks). Deterioration assessment models are effective and cost-efficient means for proactive management systems that can reduce such consequences. In this connection, this study aims to develop deterioration assessment models for sewer and stormwater pipelines in Hong Kong. First, critical factors that impact the deterioration process of these pipelines were identified. Data for these factors were then collected from the Drainage Services Department (DSD) and open-source data provided by the Hong Kong government. To improve prediction accuracy, a multi-tier concept was utilized in building the models. The first tier categorized pipelines into two groups: fail and not fail, whereas the second tier assigned a grade range from 1 to 3 to the "not fail" pipelines. Several artificial intelligence approaches, such as random forest, neural network, and SVM, were tested. Random forest achieved the highest accuracy in predicting pipelines condition, followed by neural networks. A sensitivity analysis was carried out to investigate the combined impact of two factors, with age being one of them, on the pipeline's performance. The findings of this study provide a robust decision-making tool that DSD authorities and consultants can use to optimize inspection and maintenance activities. • Critical factors impacting pipes deterioration were investigated. • Two models for assessing sewer and stormwater pipes deterioration were created. • A multi-tier concept was integrated in the models to improve prediction accuracy. • Several AI approaches e.g., ANNs, and SVM, were tested in building the models. • Model sensitivity was tested for deterioration factors impact on pipe performance. [ABSTRACT FROM AUTHOR]

Flash floods are one of the worst natural disasters, causing massive economic losses and many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk of flooding is a crucial non-structural solution for managing floods. This study aimed to assess the efficacy of combinations of the random forest (RF) model with three biology-inspired metaheuristic algorithms, namely invasive weed optimization (IWO), slime mould algorithm (SMA), and satin bowerbird optimization (SBO), for flood susceptibility mapping in Estahban town, Iran. Initially, synthetic-aperture radar (SAR) (Sentinel-1) and optical (Landsat-8) satellite images were integrated to monitor the flooded areas during the July 2022 monsoon in the study area. A dataset of 509 flood occurrence points was created to identify flood-prone areas using remote sensing techniques, considering the monitored flood areas. The dataset also included twelve flood-related criteria: topography, land cover, and climate. The holdout method was employed for modeling, with a ratio of 70:30 used for the train/test split. Data pre-processing techniques were conducted to improve model performance, including determining criteria importance and addressing multicollinearity issues using certainty factor (CF), multicollinearity, and information gain ratio (IGR) methods. Then FSM was prepared using RF, RF-IWO, RF-SBO, and RF-SMA models. The findings of this research revealed that the RF-IWO model was the best predictive model of flood susceptibility modeling, with root-mean-square-error (RMSE) (0.211 and 0.0.27), mean-absolute-error (MAE) (0.103 and 0.15), and coefficient-of-determination (R2) (0.821 and 0.707) in the training and testing phases, respectively. Receiver operating characteristic (ROC) curve analysis of FSM revealed that the most accurate models were the RF-IWO (area under the curve (AUC) = 0.983), RF-SBO (AUC = 0.979), RF-SMA (AUC = 0.963), and RF (AUC = 0.959), respectively. Integrating biology-inspired computing algorithms with machine learning algorithms presents a novel approach to enhancing the accuracy of FSMs. [Display omitted] • Detected monsoon floods and identified vulnerable areas. • Integrated optical and radar satellite images to improve flood monitoring accuracy. • The biology-inspired metaheuristic algorithms increased flood prediction accuracy. • Strong spatial association of flood probability (0.959–0.983) among models. [ABSTRACT FROM AUTHOR]

Coastal realignment is the procedure of repositioning or removing coastal defense structures to restore tidal flooding and facilitate the development of intertidal ecosystems in a previously reclaimed area from the sea. A key policy objective of coastal realignment is to increase ecosystem services provided by intertidal ecosystems and thereby contribute to human well-being. However, the social response to coastal realignment is often negative, raising the question as to what extent communities living nearby project locations recognize, value and benefit from the goods and services provided by restored intertidal ecosystems. In this study, we examine public perceptions of ecosystem services gains, losses and trade-offs associated with coastal realignment. We hereby focus on three coastal realignment case study locations in the Southwest delta, the Netherlands. Questionnaires were administered in nearby villages and the collected data (N = 261) were analyzed using random forest regression models. A notable outcome of this study is that local communities often consider coastal realignment interventions to decrease rather than increase the availability of ecosystem services. This points to a discrepancy between how coastal realignment is viewed from a policy perspective and a local community perspective. Changes in the availability of cultural ecosystem services were found to have the highest impact on the level of support for coastal realignment, while the importance attached to provisioning, regulating and supporting ecosystem services was notably lower. In consequence, to increase public support, it will be essential to minimize the loss of cultural ecosystem services, or better yet, find ways to increase cultural ecosystem services through coastal realignment, for instance by creating opportunities for recreation and tourism. • This study examines perceptions about how coastal realignment impacts ecosystem services. • Local communities associate coastal realignment with the loss of ecosystem services. • Local communities attach limited value to presumed benefits of coastal realignment from a policy perspective. • Impacts on cultural ecosystem services shape the level of support for coastal realignment. [ABSTRACT FROM AUTHOR]

Diffuse nitrogen losses from agriculture in Germany continue to cause regionally increased nitrate concentrations in groundwater. Groundwater quality monitoring cannot be a timely indicator of the effects of mitigation measures being applied in agriculture, due to frequently long transport routes and high residence times of the leachate. Instead, nitrate leaching potential is often determined at field and farm scale by monitoring soil mineral nitrogen contents at 0–90 cm depth in autumn (SMNa), i.e. before the start of the annual leachate period. In this study, we developed an understanding of the controls on the soil mineral nitrogen content at the start of winter. In an on-farm approach, extensive data was collected from 48 farms in five nitrate-sensitive regions in Germany from 2017 to 2020. From this data set, 25 management and site factors were evaluated with regard to their significance for SMNa by means of a random forest model. With the random forest regression, we identified the role of the factors on SMNa with an acceptable model accuracy with R2 = 0.56. The results show that the cultivated crop is the most important factor influencing SMNa. Potatoes, oilseed rape and maize produced the highest SMNas, whereas SMNas were lowest after spring barley, sugar beet and winter barley. Among site factors, soil type and texture as well as precipitation in October were most decisive. The effects of N fertilisation parameters such as rate and timing were masked by these site factors. The results show that the reduction of nitrogen-intensive crops in crop sequences can be a promising measure for the reduction of nitrate loads. On the other hand, our analysis makes clear that soil-related factors controlling nitrogen release and risk of leaching, as well as weather, can significantly mask the effect of cultivation. • Prediction of potential nitrate leaching with random forest algorithm. • Grown crop revealed most important factor for nitrate leaching potential. • Site-adapted nitrogen management crucial. [ABSTRACT FROM AUTHOR]

River herring (Alosa sp.) are ecologically and economically foundational species in freshwater streams, estuaries, and oceanic ecosystems. The migration between fresh and saltwater is a key life stage of river herring, where the timing and magnitude of out-migration by juveniles can be limited when streams dry and hydrologic connectivity is lost. Operational decisions by water managers (e.g., restricting community water use) can impact out-migration success; however, these decisions are largely made without reliable predictions of outmigration potential across the migration season. This research presents a model to generate short-term forecasts of the probability of herring out-migration loss. We monitored streamflow and herring out-migration for 2 years at three critical runs along Long Island Sound (CT, USA) to develop empirical understandings of the hydrologic controls on out-migration. We used calibrated Soil and Water Assessment Tool hydrologic models of each site to generate 10,000 years of daily synthetic meteorological and streamflow records. These synthetic meteorological and streamflow data were used to train random forest models to provide rapid within-season forecasts of out-migration loss from two simple predictors: current spawning reservoir depth and the previous 30-day precipitation total. The resulting models were approximately 60%–80% accurate with a 1.5-month lead time and 70–90% accurate within 2 weeks. We anticipate that this tool will support regional decisions on spawning reservoir operations and community water withdrawals. The architecture of this tool provides a framework to facilitate broader predictions of the ecological consequences of streamflow connectivity loss in human-impacted watersheds. • We developed a tool to predict juvenile river herring out-migration loss. • The framework involves random forest algorithms trained on hydrologic model outputs. • Out-migration loss can be reliably predicted 1.5 months before it occurs. [ABSTRACT FROM AUTHOR]

Over the past centuries, freshwater fish introductions and extinctions have been the major environmental and ecological crises in various water bodies in China. However, consequences of such crises on freshwater fish biodiversity in China remain only partially or locally studied. Furthermore, identifications of relatively sensitive areas along with stressors (i.e., environmental and anthropogenic drivers) influencing freshwater fish biodiversity patterns are still pending. Taxonomic, functional, and phylogenetic facets of biodiversity can well describe and evaluate the underlying processes affecting freshwater fish biodiversity patterns under different dimensionalities. Here we thus evaluated temporal changes in these facets of freshwater fish biodiversity as well as a new developed biodiversity index, multifaceted changes in fish biodiversity, for over a century at the basin level throughout China using both alpha and beta diversity approaches. We also identified the drivers influencing the changes in fish biodiversity patterns using random forest models. The results showed that fish assemblages in Northwest and Southwest China (e.g., Ili River basin, Tarim basin, and Erhai Lake basin) experienced extreme temporal and multifaceted changes in the facets of biodiversity compared with other regions, and environmental factors (e.g., net primary productivity, average annual precipitation, and unit area) largely drove these changes. Since fish faunas in over 80% of China's water bodies covering more than 80% of China's surface were currently undergoing taxonomic, functional, and phylogenetic homogenization, targeted conservation and management strategies should be proposed and implemented, especially for the areas with relatively high changes in biodiversity. [Display omitted] • A new multifaceted biodiversity index was developed to assess biodiversity impacts. • We comprehensively evaluated changes in freshwater fish biodiversity throughout China. • Extreme changes in fish biodiversity mostly occurred in Northwest and Southwest China. • Environmental factors largely drove the changes in fish biodiversity in China. • Adapted conservation and management strategies were proposed for vulnerable areas. [ABSTRACT FROM AUTHOR]

Municipal solid waste management and disposal in China have significantly evolved since 2000. Due to China's vast land area and significant socioeconomic and geographic disparities, nationwide waste management strategies need to be further evaluated. This study quantified the effect of social, economic, and geographic drivers on municipal waste disposal activities in 31 provincial-level administrative regions from 2003 to 2020, by establishing a methodology integrated by two-way fixed effects regression model, panel random forest, and spatial Durbin model. The results showed, in the past two decades, socio-economic-geo indicators significantly co-decided the landfill and incineration activities. In particular, the explanatory ability of GDP per capita was above 45%, while land resources and city size also showed great significance. Spatial autocorrelation analysis showed that the relationship between landfill/incineration rates and socio-economic-geo drivers changed notably from unobvious to significant with economic growth. Furthermore, the local economy and land resources displayed more impact than those of the neighboring regions, while sci-tech and education showed clear spatial spillover effects. Chinese government would carefully assess the full-scale promotion policy of incineration plants, landfill is still hold as a reasonable option for regions with specific socio-economic-geo conditions. [Display omitted] • Quantifying the effect socio-economic-geo drivers on municipal waste disposal. • An integrated methodology was established. • The explanatory ability of per capital GDP surpassed 45% for waste disposal. • Economic and land drivers affect the local more than its neighboring regions. • A significant spatial spill-over effect was displayed by sci-tech and education. [ABSTRACT FROM AUTHOR]

Abstract Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWR CF RF approach has a higher predictive accuracy 0.967 (96.7%) than the individual models of CF 0.763 (76.3%) and RF 0.776 (77.6%) and the CF-RF integrated model 0.897 (89.7%). Thus, the results of this research can be used by local managers and planners for environmental management. Graphical abstract Image 1 Highlights • Three approaches ((a) CF and RF; (b) integrated CF-RF; and (c) combined GWR CF RF) used for GEZM. • A new methodological framework (GWR-RF-CF) was introduced for GEZM. • Geographically Weighted Regression was used to create several homogenous units. • GWR CF RF has higher prediction accuracy than other employed models. • GWR CF RF as a new approach can be used by decision makers for GEZM. [ABSTRACT FROM AUTHOR]

Abstract Rivers are extensively regulated by damming, yet the effects of such interruption on bacterial communities have not been assessed quantitatively. To fill this gap, we proposed a bacteria-based index of biotic integrity (Ba-IBI) by using bacterial community dataset collected from the Three Gorges Reservoir and its upper reaches. Stability analysis based on bacterial resistance (RS) and resilience (RL) to external disturbance was conducted to improve the performance of the index. Four core metrics, i.e. the ratio of Bacilli, Bacteroidetes and Clostridia to Alphaproteobacteria (BBC/A), Oxalobacteraceae, Methanotrophs and Thermophiles were selected after range, responsive and redundancy tests. The improved Ba-IBI, ranging from 1.04 to 4.10, was better at distinguishing sites with or without direct dam effects compared with the unimproved one. The index values maintained high in the riverine sites while reducing in the reservoir, demonstrating the negative influence of dam construction on bacterial integrity. Based on the assessment results, 23.1%, 46.2% and 30.8% sampling sites were large, moderately and little affected by damming, respectively. A Random Forest (RF) regression model was trained and tested, offering a valid prediction of the input Ba-IBI and environmental parameters. Sensitivity analysis revealed the significant contributions of flow velocity towards the predicting process performance, indicating the importance of hydrodynamic conditions on determining the spatial variability of bacterial communities. This study provides not only a first quantitative insight for assessing bacterial response to damming, but also a guideline for applying the improved index in the dam regulation and ecological protection. Graphical abstract Image 1 Highlights • A bacteria-based index of biotic integrity can reflect bacterial response to damming. • Stability analysis was based on bacterial resistance and resilience to disturbance. • The improved index was better at distinguishing river from reservoir system. • Dam construction on riverine systems tends to reduce bacterial integrity. • Hydrodynamic condition is the major factor influencing bacterial communities. [ABSTRACT FROM AUTHOR]

Urbanization, one of the predominant trends of the 21st century, places great stress on urban water supply networks. This paper aimed to identify the most important variables driving urban water supply patterns in China, a region which has seen rapid urban growth in the last few decades. In addition, a principal component analysis-informed urban water sustainability index was developed in order to benchmark cities. The research involved applying statistical learning and other analytical methods to 12 years of urban water supply data for 627 cities across China. The findings were as follows: (1) PCA showed that approximately 46.8% of variability in the data could be explained by two principal components. Component 1 (37.26%) was more closely associated with variables related to water supply and sale, supply pipelines, and water supply finance. C2 (9.51%) was clearly related to urban water prices and average per capita water use. (2) Random forest and XGBoost algorithms were effective in classifying cities according to their region, with model testing accuracies of 87.69% and 88.32% respectively. (3) Chinese cities have consistently suffered water loss/leakage rates above 20% since 2001, and water prices are closely associated with leakage. (4) China's urban water sustainability has increased by just 3.56% between 2001 and 2013; Southwest China saw the highest growth rate in urban water supply sustainability. The implications of our research effort will be useful for decision makers in water-stressed urban areas around the world who are seeking novel insights in how to leverage statistical learning techniques to gain insights into urban drinking water supply patterns. [ABSTRACT FROM AUTHOR]

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. The key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future. • We examine the factors influencing inbound paper contamination in MRFs. • Importance of the factors are determined by three machine learning models. • The contamination levels in single stream were found to be higher than that of commingled. • SVM regressor obtained the best performance. • Population size is determined to be the most significant factor by all three ML methods. [ABSTRACT FROM AUTHOR]

As a national pilot city for solid waste disposal and resource reuse, Dongguan in Guangdong Province aims to vigorously promote the high-value utilization of solid waste and contribute to the sustainable development of the Greater Bay Area. In this study, life cycle assessment (LCA) coupled with principal component analysis (PCA) and the random forest (RF) algorithm was applied to assess the environmental impact of multi-source solid waste disposal technologies to guide the environmental protection direction. In order to improve the technical efficiency and reduce pollution emissions, some advanced technologies including carbothermal reduction‒oxygen-enriched side blowing, directional depolymerization‒flocculation demulsification, anaerobic digestion and incineration power generation, were applied for treating inorganic waste, organic waste, kitchen waste and household waste in the park. Based on the improved techniques, we proposed a cyclic model for multi-source solid waste disposal. Results of the combined LCA-PCA-RF calculation indicated that the key environmental load type was human toxicity potential (HTP), came from the technical units of carbothermal reduction and oxygen-enriched side blowing. Compared to the improved one, the cyclic model was proved to reduce material and energy inputs by 66%–85% and the pollution emissions by 15%–88%. To sum up, the environmental impact assessment and systematic comparison suggest a cyclic mode for multi-source solid waste treatments in the park, which could be promoted and contributed to the green and low-carbon development of the city. • LCA, PCA, and RF methods are coupled to evaluate the environmental impacts of multi-source solid waste treatment. • Human toxicity potential was the key environmental load within the recycling park. • Cyclic model provides enhanced energy conservation and emission reduction ability. [ABSTRACT FROM AUTHOR]

Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate. For prediction, we used a set of fifteen causative factors as a cross-function of soil, site and stand-related predictors. A total of 80 sample plots, including 1134 affected trees, were surveyed and used for the modeling process at the 5600-ha landscape level of the southern Zagros, Iran, where the disease occurs in roughly 25% of forest lands. Ten machine learning algorithms were explored and the performance of each algorithm to predict oak disease outbreak was evaluated. The modeling framework used maximum entropy to remove the least influential variables and build the status-quo management scenario to which the results of the prediction models were compared. Results showed that the random forests algorithm (AUC = 0.96: Precision = 0.71: Accuracy = 0.90: F-Measure = 0.70) achieved significantly better results than the status-quo management (Precision = 0.13: Accuracy = 0.67: F-Measure = 0.12) and any other algorithm. Soil chemical properties (NPK, organic carbon and EC) and landform predictors (slope, distance to roads, and TWI) were major forecasters of oak disease outbreak identified by the random forest algorithm. Geostatistical analysis enabled the creation of a map that identified sites at higher risk of infestation, allowing epidemiologists and forest managers to find sites likely to be infested. Consequently, financial resources can be allocated and management practices such as sanitation felling treatments applied across large forest landscapes to minimize the risk of spread and severity to uninfested high-value trees on nearby or adjacent land zones that are in the early stage of epidemics. • Soil and landform attributes explain short-term presence of oak charcoal disease. • Random forest (RF) algorithms outperform nine other machine learning algorithms. • RF predicts 41% of Brant's oak at moderate disease intensity in southern Zagros. • RF algorithm predicts oak dieback, decline or mortality with 96% accuracy. • Machine-learning algorithms predict spatial charcoal disease intensity in Brant's oak. [ABSTRACT FROM AUTHOR]

Landscape structure and vegetation coverage are important habitat conditions for Oriental Migratory Locust infestation in East Asia. Characterizing the landscape's dynamics of locust habitat is meaningful for reducing the occupation of locusts and limiting potential risks. To better understand causes and consequences of landscape pattern and locust habitat, it is not enough to simply detect locust habitat of each year. Rather, landcover transitions causing the change of locust habitat area must also be explored. This paper proposes an integrated implement to quantify the influence of landscape's dynamics on locust habitat changes based on three tenets: 1) temporal context can provide insight into the land cover transitions, 2) the detection of locust habitat area is operated on patches rather than pixels with full consideration of landscape's ecology, 3) the modeling must be flexible and unsupervised. These ideas have not been previously explored in demonstrating the possible role of changes in landscape characteristics to drive locust habitat transitions. The case study focuses on the Dagang district, a hot spot of locust infestation of China, from 2000 to 2015. Firstly, the seasonal characteristics of typical landcovers in NDVI, TVI, and LST were extracted from fused Landsat-MODIS surface reflectance imagery. Subsequently, a landscape membership-based random forest (LMRF) algorithm was proposed to quantify the landscape structure and hydrological regimen of locust habitat at the patch level. Finally, we investigated the correlations between the specific landcover transitions and habitat changes. Within the 16 years observations, our findings suggest that the sparse reeds and weeds in the vicinity of beach land, riverbanks, and wetlands are the dominant landscape structure associated with locust habitat change (R 2  > 0.68), and the fluctuation in the water level is a key ecological factor to facilitate the locust habitat change (R 2  > 0.61). These results are instrumental for developing precision pesticide use to reduce environmental degradation, and providing positive perspectives for ecological management and transformation of locust habitats. [ABSTRACT FROM AUTHOR]

Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wrocław. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April–September) and for the summer season (June–August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM 2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM 2.5 ) were found to have no significant effect on the concentrations of the studied pollutants. [ABSTRACT FROM AUTHOR]

Cumulative effects assessment (CEA) ― a type of environmental appraisal ― lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regression trees, which model response variables by recursive partitioning. Random forests were used to model a series of candidate ecological indicators that described lakes and rivers from a case study watershed (The Muskoka River Watershed, Canada). Suitability of the candidate indicators for use in cumulative effects assessment and watershed monitoring was assessed according to how well they could be predicted from natural habitat features and how sensitive they were to human land-use. The best models explained 75% of the variation in a multivariate descriptor of lake benthic-macroinvertebrate community structure, and 76% of the variation in the conductivity of river water. Similar results were obtained by cross-validation. Several candidate indicators detected a simulated doubling of urban land-use in their catchments, and a few were able to detect a simulated doubling of agricultural land-use. The paper demonstrates that random forests can be used to describe the combined and singular effects of multiple stressors and natural environmental factors, and furthermore, that random forests can be used to evaluate the performance of monitoring indicators. The numerical methods presented are applicable to any ecosystem and indicator type, and therefore represent a step forward for CEA. [ABSTRACT FROM AUTHOR]

Invasive plant species pose a direct threat to biodiversity and ecosystem services. Among these, Rosa rugosa has had a severe impact on Baltic coastal ecosystems in recent decades. Accurate mapping and monitoring tools are essential to quantify the location and spatial extent of invasive plant species to support eradication programs. In this paper we combined RGB images obtained using an Unoccupied Aerial Vehicle, with multispectral PlanetScope images to map the extent of R. rugosa at seven locations along the Estonian coastline. We used RGB-based vegetation indices and 3D canopy metrics in combination with a random forest algorithm to map R. rugosa thickets, obtaining high mapping accuracies (Sensitivity = 0.92, specificity = 0.96). We then used the R. rugosa presence/absence maps as a training dataset to predict the fractional cover based on multispectral vegetation indices derived from the PlanetScope constellation and an Extreme Gradient Boosting algorithm (XGBoost). The XGBoost algorithm yielded high fractional cover prediction accuracies (RMSE = 0.11, R2 = 0.70). An in-depth accuracy assessment based on site-specific validations revealed notable differences in accuracy between study sites (highest R2 = 0.74, lowest R2 = 0.03). We attribute these differences to the various stages of R. rugosa invasion and the density of thickets. In conclusion, the combination of RGB UAV images and multispectral PlanetScope images is a cost-effective method to map R. rugosa in highly heterogeneous coastal ecosystems. We propose this approach as a valuable tool to extend the highly local geographical scope of UAV assessments into wider areas and regional evaluations. • UAV rgb data enables accurate monitoring of Rosa rugosa in coastal areas. • Photogrammetric canopy metrics play a key role in the detection of Rosa rugosa. • UAV and PlanetScope data fusion accurately predicts Rosa rugosa over large areas. [ABSTRACT FROM AUTHOR]

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (A ustralia-wide M achine L earning ET for T rees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R2 of 0.94 (RMSE 6.63 mm period−1) and 0.92 (RMSE 6.89 mm period−1), respectively. In comparison, correlation between in-situ ET and a national ET product returned R2 of 0.44 (RMSE 34.08 mm period−1) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function. [Display omitted] • Data to quantify riverine tree evapotranspiration is underpinned by high replication of in-situ sap flow measurements. • Evapotranspiration from various field environments is reproduced using in-situ data and machine learning - R2 of 0.94 • Spatiotemporal data created to report long-term woody tree evapotranspiration (from 2012) in monthly intervals. • Random Forest model includes environmental parameters to estimate evapotranspiration across a variety of environments. [ABSTRACT FROM AUTHOR]

Stand productivity research has mainly focused on increasing yield and has recently begun to examine changes in carbon storage. The Korean government is interested in finding ways to increase forest carbon capture to meet carbon neutrality requirements because approximately 63% of the land is covered by forests. In addition, 69% of these forests are older than 30 years old and their productivity and aboveground carbon storage rates are expected to decline. The purpose of this study was to examine the effect of quadratic mean diameter (QMD), stand basal area, site index, slope, climate (MAT and MAP), stand age, stand structural diversity, and stand composition on the productivity of aging Korean red pine-dominated stands. Based on the effects of these factors, we explored how to manage pine forests with the focus of increasing their productivity. Random forest regression was used for the analysis, and periodic basal area increment (PBAI) was used as the dependent variable of stand productivity. Our results show that the most influential factor on stand productivity was QMD. PBAI dramatically decreased from approximately 0.8 to 0.53 m2/ha·year as QMD increased up to 18 cm. Since diameter (QMD) increment is closely associated with changes in tree competition; increasing tree competition with increasing QMD and stand basal area may lead to decreases in PBAI owing to decreases in growth rate due to space and resource limitations and increases in mortality. PBAI decreased when basal area increased from 22 to 51.5 m2/ha. PBAI increased for site index values between 8 and 12.5 m and decreased for stand age values up to approximately 31 years. For climate factors, PBAI generally increased with increasing MAP and slightly increased as MAT increased up to approximately 11.2 °C and then decreased at higher MAT. PBAI initially increased with increasing slope values, decreased with values lower than 15°, and remained stable at slope values in the range of 16–34°. Stand structural diversity, which ranged from 1.32 to 1.62, exhibited a similar negative influence on PBAI associated with increasing stand density. With regard to pine composition, pine stands with a large proportion of pine basal area, showed higher productivity due to the simple stand structure resulting in better growth of shade intolerant pine. This study found that stand density increases with the development of pine stands and that density increases had negative influences on stand productivity. Collectively, our results suggest that stand density management is essential for increasing stand productivity and carbon sequestration in the Korean red pine-dominated stands of South Korea. • Random forest regression analysis was used to examine factors influencing productivity. • Tree size (QMD) was the most influential factor of pine stand productivity. • QMD, basal area, and structural diversity increased with increasing stand age. • Increases in stand density with stand development decreased stand productivity. • Stand density management is essential to increase productivity of Korean pine stands. [ABSTRACT FROM AUTHOR]

Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation. [Display omitted] • KS approach provides an algorithmic framework that is not prone to overfitting. • KS approach has been applied in an extremely divergent case of P. edulis forests. • KS can be used for estimating unknown CSSs by datasets with missing data. • The study compiled the climatic classification table with CSSs for P. edulis forests. • Quantifying unobservable variables is a key for chaotic estimation in the future. [ABSTRACT FROM AUTHOR]