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Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion–extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future. [ABSTRACT FROM AUTHOR]

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human–machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models—a kinematic extrapolation algorithm and a Random Forest machine learning algorithm—when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device. [ABSTRACT FROM AUTHOR]

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998–2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km2 as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Building Performance Simulation extensively uses statistical learning techniques for quicker insights and improved accessibility. These techniques help understand the relationship between input variables and the desired outputs, and they can predict unknown observations. Prediction becomes more informative with uncertainty quantification, which involves computing prediction intervals. Conformal prediction has emerged over the past 25 years as a flexible and rigorous method for estimating uncertainty. This approach can be applied to any pre-trained model, creating statistically rigorous uncertainty sets or intervals for model predictions. This study uses data from simulated buildings to demonstrate the powerful applications of conformal prediction in Building Performance Simulation (BPS) and, consequently, to the broader energy sector. Results show that conformal prediction can be applied when any assumptions about input and output variables are made, enhancing understanding and facilitating informed decision-making in energy system design and operation. [ABSTRACT FROM AUTHOR]

Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains. [ABSTRACT FROM AUTHOR]

Accurate prediction of lake surface water temperature (LSWT) is essential for understanding the impacts of climate change on aquatic ecosystems and for guiding environmental management strategies. Predictions of LSWT for two prominent lakes in northern China, Qinghai Lake and Hulun Lake, under various future climate scenarios, were conducted in the present study. Utilizing historical hydrometeorological data and MODIS satellite observations (MOD11A2), we employed three advanced machine learning models—Random Forest (RF), XGBoost, and Multilayer Perceptron Neural Network (MLPNN)—to predict monthly average LSWT across three future climate scenarios (ssp119, ssp245, ssp585) from CMIP6 projections. Through the comparison of training and validation results of the three models across both lake regions, the RF model demonstrated the highest accuracy, with a mean MAE of 0.348 °C and an RMSE of 0.611 °C, making it the most optimal and suitable model for this purpose. With this model, the predicted LSWT for both lakes reveals a significant warming trend in the future, particularly under the high-emission scenario (ssp585). The rate of increase is most pronounced under ssp585, with Hulun Lake showing a rise of 0.55 °C per decade (R2 = 0.72) and Qinghai Lake 0.32 °C per decade (R2 = 0.85), surpassing trends observed under ssp119 and ssp245. These results underscore the vulnerability of lake ecosystems to future climate change and provide essential insights for proactive climate adaptation and environmental management. [ABSTRACT FROM AUTHOR]

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. [ABSTRACT FROM AUTHOR]

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development. [ABSTRACT FROM AUTHOR]

Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. For this purpose, we apply the Cuckoo open-source sandbox system, carefully configured for the production of a novel dataset for dynamic malware analysis containing 22,200 annotated samples (11,735 benign and 10,465 malware). Each sample represents a full-featured report generated by the Cuckoo sandbox when a corresponding binary file is submitted for analysis. To support our position that the discriminative power of the full-featured sandbox reports is greater than the discriminative power of just API call sequences, we consider samples obtained from binary files whose execution induced API calls. In addition, we derive an additional dataset from samples in the full-featured dataset, whose samples contain only information on API calls. In a three-way factorial design experiment (considering the feature set, the feature representation technique, and the random forest model hyperparameter settings), we trained and tested a set of random forest models in a two-class classification task. The obtained results demonstrate that resorting to full-featured sandbox reports improves malware detection performance. The accuracy of 95.56 percent obtained for API call sequences was increased to 99.74 percent when full-featured sandbox reports were considered. [ABSTRACT FROM AUTHOR]

The energy consumption of air-jet looms mainly comes from air compressors. Predicting the air consumption of air-jet looms for the upcoming period is significant for the variable frequency adjustment of air compressors, thereby aiding in energy saving and reducing fabric costs. This paper proposes an innovative method that combines Sliding Time Windows (STW), feature analysis, and incremental learning to improve the accuracy of short-term air consumption prediction. First, the STW method is employed during the data collection phase to enhance data reliability. Through feature analysis, significant factors affecting the air consumption of air-jet looms, beyond traditional research, are explored and incorporated into the prediction model. The experimental results indicate that the introduction of new features improved the model's R2 from 0.905 to 0.950 and reduced the MSE from 32.369 to 16.239. The STW method applied to the same random forest model increased the R2 from 0.906 to 0.950 and decreased the MSE from 32.244 to 16.239. The decision tree method, compared to the linear regression model, improved the R2 from 0.928 to 0.950 and reduced the MSE from 23.541 to 16.239, demonstrating significant predictive performance enhancement. After establishing the optimal model, incremental learning is used to continuously improve the reliability and accuracy of short-term predictions. Experiments show that the incremental learning method, compared to static models, offers better resilience and reliability when new data are collected. The proposed method significantly improves the accuracy of air consumption prediction for air-jet looms, providing strong support for the variable frequency adjustment of air compressors, and contributes to the goals of energy saving and cost reduction. The research results indicate that this method not only enhances prediction accuracy but also provides new insights and methods for future energy-saving research. [ABSTRACT FROM AUTHOR]

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field. [ABSTRACT FROM AUTHOR]

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies. [ABSTRACT FROM AUTHOR]

Machine learning methods have improved in recent years and provide increasingly powerful tools for understanding landscape evolution. In this study, we used the random forest method based on Google Earth Engine to evaluate the desertification dynamics in northern China from 1995 to 2020. We selected Landsat series image bands, remote sensing inversion data, climate baseline data, land use data, and soil type data as variables for majority voting in the random forest method. The method's average classification accuracy was 91.6% ± 5.8 [mean ± SD], and the average kappa coefficient was 0.68 ± 0.09, suggesting good classification results. The random forest classifier results were consistent with the results of visual interpretation for the spatial distribution of different levels of desertification. From 1995 to 2000, the area of aeolian desertification increased at an average rate of 9977 km2 yr−1, and from 2000 to 2005, from 2005 to 2010, from 2010 to 2015, and from 2015 to 2020, the aeolian desertification decreased at an average rate of 2535, 3462, 1487, and 4537 km2 yr−1, respectively. [ABSTRACT FROM AUTHOR]

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin. [ABSTRACT FROM AUTHOR]

Artificial intelligence seems to be a new point of view to classical problems that, in the past, could not be understood in depth, leaving certain gaps in each knowledge area. As an example of this, maritime accidents are one of the most recognised international problems, with clear environmental and human life consequences. From the beginning, statistical studies have shown that not only the typical sampled variables must be considered but the accidents are related to human factors that, at the same time, are related to some variables like fatigue that cannot be easily sampled. In this research work, the use of machine learning algorithms on over 300 maritime accidents is proposed to identify the relationship between human factors and the main variables. The results showed that compliance with the minimum crew members and ship length are the two most relevant variables related to each accident for the Spanish Search and Rescue (SAR) region, as well as the characteristics of the ships. These accidents could be understood as three main groups of accidents related to the general tendency to not meet the minimum number of crew members and its difference in the year of construction of the ship. Finally, it was possible to use neural networks to model accidents with sufficient accuracy (determination factor higher than 0.60), which is particularly interesting in the context of a control system for maritime transport. [ABSTRACT FROM AUTHOR]

Soil classification is essential for understanding soil properties and their suitability for conveying the characteristics of soil types. In this study, we present a prediction of soil classification using fewer soil variables by employing the random forest (RF) technique in machine learning. This study compiled the parameters outlined in the unified soil classification system (USCS), a widely used method for categorizing soils based on their properties and behavior. These parameters, encompassing grain size distribution, Atterberg limits, the coefficient of uniformity, and the coefficient of curvature, were defined within specific ranges to create a synthetic database for training the RF model. The importance of input factors in soil classification was assessed using the out-of-bag samples in RF. Through rigorous validation techniques, including cross-validation, the performance of the RF model is thoroughly assessed, demonstrating its capability to accurately evaluate soil classification. The findings indicate that the RF model presented in this study exhibits a promising alternative, providing automated and accurate classification based on soil data. Notably, the model indicates that the coefficients of uniformity and gradation are insignificant for soil classification and can predict soil types even when these factors are missing, a feat that traditional methods struggle to achieve. [ABSTRACT FROM AUTHOR]

Accurate land-use and land-cover (LULC) mapping is crucial for effective watershed management and hydrological modeling in arid regions. This study examines the use of high-resolution PlanetScope imagery for LULC mapping, change detection, and hydrological modeling in the Wadi Ham watershed, Fujairah, UAE. The authors compared LULC maps derived from Sentinel-2 and PlanetScope imagery using maximum likelihood (ML) and random forest (RF) classifiers. Results indicated that the RF classifier applied to PlanetScope 8-band imagery achieved the highest overall accuracy of 97.27%. Change detection analysis from 2017 to 2022 revealed significant transformations, including a decrease in vegetation from 3.371 km2 to 1.557 km2 and an increase in built-up areas from 3.634 km2 to 6.227 km2. Hydrological modeling using the WMS-GSSHA model demonstrated the impact of LULC map accuracy on simulated runoff responses, with the most accurate LULC dataset showing a peak discharge of 1160 CMS at 930 min. In contrast, less accurate maps showed variations in peak discharge timings and magnitudes. The 2022 simulations, reflecting urbanization, exhibited increased runoff and earlier peak flow compared to 2017. These findings emphasize the importance of high-resolution, accurate LULC data for reliable hydrological modeling and effective watershed management. The study supports UAE's 2030 vision for resilient communities and aligns with UN Sustainability Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action), highlighting its broader relevance and impact. [ABSTRACT FROM AUTHOR]

Arsenic (As) contamination in groundwater represents a major global health threat, potentially impacting billions of individuals. Elevated As concentrations are found in river floodplains across south and southeast Asia, as well as in the inland basins of China, despite varying sedimentological and hydrogeochemical conditions. The specific mechanisms responsible for these high As levels remain poorly understood, complicating efforts to predict and manage the contamination. Applying hydro-chemical, geological, and soil parameters as explanatory variables, this study employs multiple linear regression (MLIR) and random forest regression (RFR) models to estimate groundwater As concentrations in these regions. Additionally, random forest classification (RFC) and multivariate logistic regression (MLOR) models are applied to predict the probability of As levels exceeding 10 μg/L in the Hetao Basin (China) and Bangladesh. Model validation reveals that RFR explains 80% and 70% of spatial variability of As concentration in the Hetao Basin and Bangladesh, respectively, outperforming MLIR, which accounts for only 35% and 32%. Similarly, RFC outperforms MLOR in predicting high As probability, achieving correct classification rates of 98.70% (Hetao Basin) and 98.25% (Bangladesh) on training datasets, and 82.76% (Hetao Basin) and 91.20% (Bangladesh) on validation datasets. The performance of the MLOR model on the validation set yields accuracy rates of 81.60% and 72.18%, respectively. In the Hetao Basin, Ca2+, redox potential (Eh), Fe, pH, SO42−, and Cl− are key predictors of As contamination, while in Bangladesh, soil organic carbon (SOC), pH, and SO42− are significant predictors. This study underscores the potential of random forest (RF) models as robust tools for predicting groundwater As contamination. [ABSTRACT FROM AUTHOR]

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R2 = 0.61), while the support vector machine performed best in predicting electron transport rate (R2 = 0.57), Fv/Fm ratio (R2 = 0.66) and stomatal conductance (R2 = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R2 = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change. [ABSTRACT FROM AUTHOR]

Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial sensors have been used to perform subtask segmentation on functional mobility tests, providing further clinically useful measures such as fall risk. However, L test subtask segmentation rule-based algorithms developed for able-bodied individuals have not produced sufficiently acceptable results when tested with lower-limb amputee data. In this paper, a random forest machine learning model was trained to segment subtasks of the L test for application to lower-limb amputees. The model was trained with 105 trials completed by able-bodied participants and 25 trials completed by lower-limb amputee participants and tested using a leave-one-out method with lower-limb amputees. This algorithm successfully classified subtasks within a one-foot strike for most lower-limb amputee participants. The algorithm produced acceptable results to enhance clinician understanding of a person's mobility status (>85% accuracy, >75% sensitivity, >95% specificity). [ABSTRACT FROM AUTHOR]

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated. [ABSTRACT FROM AUTHOR]

Vegetation indices are widely used to assess vegetation dynamics. The Normalized Vegetation Index (NDVI) is the most widely used metric in agriculture, frequently as a proxy for different physiological and agronomical aspects, such as crop yield or biomass, crop density, or drought stress. Much effort has therefore been directed to NDVI forecasting, which is usually correlated with precipitation. However, in Mediterranean and arid climates, the relationship is more complex due to prolonged dry periods and sparse precipitation events. In this study, we forecast the NDVI 7 and 30 days ahead for Mediterranean permanent grasslands using a machine learning Random Forest (RF) model for the period from 2015 to 2022. The model compares two soil moisture products as predictors: simulated soil moisture values based on in situ soil moisture sensor observations and remote sensing-derived observations of Soil Water Index (SWI) values. We further analyzed the anomalies of the predicted NDVI using the z-score. The results show that both products can be used as reliable predictors for permanent grasslands in Mediterranean areas. Predictions at 7 days are more accurate and better forecast the negative effect of drought on vegetation dynamics than those at 30 days. This study shows the potential of using a simple methodology and readily available data to predict the grassland growth dynamics in the Mediterranean area. [ABSTRACT FROM AUTHOR]

Building height (BH) estimation is crucial for urban spatial planning and development. BH estimation using digital surface model data involves obtaining ground and roof elevations. However, vegetation and shadows around buildings affect the selection of the required elevation, resulting in large BH estimation errors. In highly urbanized areas, buildings of similar heights often have similar characteristics and spatial proximity, which have reference significance in BH estimation but are rarely utilized. Herein, we propose a BH estimation method based on BIRCH clustering and a random forest (RF) model. We obtain the initial BH results using a method based on the optimal ground search area and a multi-index evaluation. BIRCH clustering and an RF classification model are used to match buildings of similar heights based on their spatial distance and attribute characteristics. Finally, the BH is adjusted based on the ground elevation obtained from the secondary screening and the BH matching. The validation results from two areas with over 12,000 buildings show that the proposed method reduces the root-mean-square error of the final BH results compared with the initial results. Comparing the obtained height maps shows that the final results produce a relatively accurate BH in areas with high shading and vegetation coverage, as well as in areas with dense buildings. Thus, the proposed method has been validated for its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

CO2 emissions prediction plays a key role in atmospheric environment management and regional sustainable development. Taking the Four Provinces of Mountains and Rivers (Henan, Hebei, Shandong, and Shanxi) in China as an example, the Autoregressive Integrated Moving Average Model (ARIMA) and random forest importance analysis were used to calculate the future trend of the CO2 emission–influencing factors and obtain the main influencing factors. Based on the above, BP neural network (BPNN), support vector machine (SVR), and random forest (RF) models were used to predict the future apparent CO2 emissions of the four provinces. The results show that, in general, population, coal consumption, and per capita GDP are the main factors influencing CO2 emissions. The RF model has the best prediction performance; for instance, RMSE (81.86), R2 (0.905), and MAE (64.69). The prediction results show that the total apparent CO2 emissions of the Four Provinces of Mountains and Rivers will peak in 2028 (with a peak of about 4500 Mt). The apparent CO2 emissions of Henan, Hebei, and Shandong Province peaked in 2011 (with a peak of about 654 Mt), 2013 (with a peak of about 657 Mt), and 2020 (with a peak of about 1273 Mt), respectively. Shanxi is forecast to reach its peak (with a peak of about 2486 Mt) in 2029. The apparent CO2 emissions of all provinces showed an obvious downward trend after reaching their peak. Henan, Hebei Shandong, and Shanxi showed a significant downward trend in 2018, 2023, and 2032, respectively. [ABSTRACT FROM AUTHOR]

How to decouple economic growth from carbon dioxide emissions and achieve low-carbon transformation of the Chinese economy has become an urgent problem that needs to be solved. Firstly, the Tapio index is used to identify China's carbon peak status, and then the Technology Choice Index (TCI) and economic complexity are introduced into the comprehensive factor analysis framework for carbon dioxide emissions. Key influencing factors are identified using random forest and ridge regression. On this basis, a novel sparrow search algorithm–long short-term memory (SSA-LSTM) model which has more prediction accuracy compared with past studies is constructed to predict the dynamic evolution trend of carbon dioxide emissions, and in combination with scenario analysis, the path towards the carbon peak is simulated. The following conclusions are obtained: The benchmark scenario peaks in 2031, with a peak of 12.346 billion tons, and the low-carbon scenario peaks in 2030, with a peak of 11.962 billion tons. The extensive scenario peaks in 2037, with a peak of 13.291 billion tons. Under six scenarios, it can be concluded that energy intensity is the key factor in reducing the peak. These research results provide theoretical support for decision-makers to formulate emission reduction policies and adjust the carbon peak path. [ABSTRACT FROM AUTHOR]

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Out of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing. [ABSTRACT FROM AUTHOR]

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively. [ABSTRACT FROM AUTHOR]

Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features. [ABSTRACT FROM AUTHOR]

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain. [ABSTRACT FROM AUTHOR]

Background: Liposomal amphotericin B (L-AmB) has become the mainstay of treatment for severe invasive fungal infections. However, the potential for renal toxicity must be considered. Aims: To evaluate the incidence of acute kidney injury (AKI) in critically ill patients receiving L-AmB for more than 48 h. Methods: Retrospective, observational, single-center study. Clinical, demographic and laboratory variables were obtained automatically from the electronic medical record. AKI incidence was analyzed in the entire population and in patients with a "low" or "high" risk of AKI based on their creatinine levels at the outset of the study. Factors associated with the development of AKI were studied using random forest models. Results: Finally, 67 patients with a median age of 61 (53–71) years, 67% male, a median SOFA of 4 (3–6.5) and a crude mortality of 34.3% were included. No variations in serum creatinine were observed during the observation period, except for a decrease in the high-risk subgroup. A total of 26.8% (total population), 25% (low risk) and 13% (high risk) of patients developed AKI. Norepinephrine, the SOFA score, furosemide (general model), potassium, C-reactive protein and procalcitonin (low-risk subgroup) were the variables identified by the random forest models as important contributing factors to the development of AKI other than L-AmB administration. Conclusions: The development of AKI is multifactorial and the administration of L-AmB appears to be safe in this group of patients. [ABSTRACT FROM AUTHOR]

Over three-quarters of Taiwan's landmass consists of mountainous slopes with steep gradients, leading to frequent rockfall hazards that obstruct traffic and cause injuries and fatalities. This study used Google Alerts to compile internet news on rockfall incidents along Taiwan's highway system from April 2019 to February 2024. The locations of these rockfalls were geolocated using Google Earth and integrated with geographical, topographical, environmental, geological, and socioeconomic variables. Employing machine learning algorithms, particularly the Random Forest algorithm, we analyzed the potential for rockfall hazards along roadside slopes. The model achieved an overall accuracy of 0.8514 on the test dataset, with a sensitivity of 0.8378, correctly identifying 83.8% of rockfall locations. Shapley Additive Explanations (SHAP) analysis highlighted that factors such as slope angle and distance to geologically sensitive areas are pivotal in determining rockfall locations. The study underscores the utility of internet-based data collection in providing comprehensive coverage of Taiwan's highway system, and enabled the first broad analysis of rockfall hazard susceptibility for the entire highway network. The consistent importance of topographical and geographical features suggests that integrating detailed spatial data could further enhance predictive performance. The combined use of Random Forest and SHAP analyses offers a robust framework for understanding and improving predictive models, aiding in the development of effective strategies for risk management and mitigation in rockfall-prone areas, ultimately contributing to safer and more reliable transportation networks in mountainous regions. [ABSTRACT FROM AUTHOR]

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models' performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. [ABSTRACT FROM AUTHOR]

In the field of bolt loosening detection, although some progress has been made, there are still challenges such as high operational complexity, single feature extraction methods, and insufficient analysis model performance, especially in large steel truss structures, where there is a lack of efficient and accurate bolt loosening identification solutions. In response to these shortcomings, this article proposes an innovative bolt loosening damage recognition method based on sound signals. This method integrates feature extraction techniques of Mel frequency cepstral coefficients (MFCCs) and wavelet packet energy spectra (WPES), and comprehensively characterizes sound signals by constructing MFCC-WPES combined features. Subsequently, the random forest (RF) algorithm optimized by genetic algorithm was used for feature selection and model training, aiming to improve recognition accuracy and robustness. The experimental results show that this method can not only accurately identify bolt loosening signals in steel truss structure bolt loosening detection, but also has strong identification ability for environmental noise. Compared with traditional methods, the proposed solution in this article shows significant improvements in both performance and practicality, providing a new perspective and solution for the technological advancement of bolt loosening detection in steel truss structures. [ABSTRACT FROM AUTHOR]

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet has facilitated expression, sharing, and interaction on social networks, but some speech may contain harmful discrimination. Therefore, it is crucial to classify such speech. In this paper, we collected discriminatory data from Sina Weibo and propose the improved Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on Latent Dirichlet Allocation (LDA) to improve data quality and balance. And we propose a new integration method integrating Support Vector Machine (SVM) and Random Forest (RF). The experimental results demonstrate that the integrated model exhibits enhanced precision, recall, and F1 score by 6.0%, 5.4%, and 5.7%, respectively, in comparison with SVM alone. Moreover, it exhibits the best performance in comparison with other machine learning methods. Furthermore, the positive impact of improved SMOTE and this integrated method on model classification is also confirmed in ablation experiments. [ABSTRACT FROM AUTHOR]

The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data. [ABSTRACT FROM AUTHOR]

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes. [ABSTRACT FROM AUTHOR]

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910−2, the local regression 9.2110−2, and the random forest 1.96 × 10−2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power. [ABSTRACT FROM AUTHOR]

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods. [ABSTRACT FROM AUTHOR]

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. [ABSTRACT FROM AUTHOR]

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. [ABSTRACT FROM AUTHOR]

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of "generating random sample points—random forest model training—winter wheat extraction"; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future. [ABSTRACT FROM AUTHOR]

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration. [ABSTRACT FROM AUTHOR]

Metamaterials, characterized by unique structures, exhibit exceptional properties applicable across various domains. Traditional methods like experiments and finite-element methods (FEM) have been extensively utilized to characterize these properties. However, exploring an extensive range of structures using these methods for designing desired structures with excellent properties can be time-intensive. This paper formulates a machine-learning-based approach to expedite predicting effective metamaterial properties, leading to the discovery of microstructures with diverse and outstanding characteristics. The process involves constructing 2D and 3D microstructures, encompassing porous materials, solid–solid-based materials, and fluid–solid-based materials. Finite-element methods are then employed to determine the effective properties of metamaterials. Subsequently, the Random Forest (RF) algorithm is applied for training and predicting effective properties. Additionally, the Aquila Optimizer (AO) method is employed for a multiple optimization task in inverse design. The regression model generates accurate estimation with a coefficient of determination higher than 0.98, a mean absolute percentage error lower than 0.088, and a root mean square error lower than 0.03, indicating that the machine-learning-based method can accurately characterize the metamaterial properties. An optimized structure with a high Young's modulus and low thermal conductivity is designed by AO within the first 30 iterations. This approach accelerates simulating the effective properties of metamaterials and can design microstructures with multiple excellent performances. The work offers guidance to design microstructures in various practical applications such as vibration energy absorbers. [ABSTRACT FROM AUTHOR]

Classrooms play a pivotal role in students' learning, and maintaining optimal indoor air quality is crucial for their well-being and academic performance. Elevated CO2 levels can impair cognitive abilities, underscoring the importance of accurate predictions of CO2 concentrations. To address the issue of inadequate analysis of factors affecting classroom CO2 levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO2 levels to enhance predictions, including occupancy, temperature, humidity, and other relevant factors. It combines three key components: random forest (RF), tree-structured Parzen estimator (TPE), and long short-term memory (LSTM). By leveraging these techniques, our model enhances the predictive capabilities and refines itself through Bayesian optimization using TPE. Experiments conducted on a self-collected dataset of classroom CO2 concentrations and influencing factors demonstrated significant improvements in the MAE, RMSE, MAPE, and R2. Specifically, the MAE, RMSE, and MAPE were reduced to 2.96, 5.54, and 0.60%, respectively, with the R2 exceeding 98%, highlighting the model's effectiveness in assessing indoor air quality. [ABSTRACT FROM AUTHOR]

The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to landslides or demonstrate spatial biases, leading to differences in model predictions. To address the impact of non-landslide sample selection on landslide susceptibility predictions, this study uses the western region of Henan Province as a case study. Utilizing historical data, remote sensing interpretation, and field surveys, a sample dataset comprising 834 landslide points is obtained. Ten environmental factors, including elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road, are chosen to establish an evaluation index system. Negative sample sampling areas are delineated based on the susceptibility assessment outcomes derived from the information value model. Two sampling strategies, whole-region random sampling (I) and partition-based random sampling (II), are employed. Random Forest (RF) and Back Propagation Neural Network (BPNN) models are used to forecast and delineate landslide susceptibility in the western region of Henan Province, with prediction accuracy evaluated. The model prediction accuracy is ranked as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). Under the Receiver Operating Characteristic (AUC) curve and accuracy, the II-RF and II-BPNN models exhibit increases in the region by 12.17% and 15.61%, respectively, compared to the I-RF and I-BPNN models. Moreover, the II-BPNN model shows improvements over the I-BPNN model with increases in AUC and accuracy by 14.54% and 16.52%, respectively. This indicates enhancements in model performance and predictive capability. In terms of recall and specificity, the II-RF and II-BPNN models demonstrate increases in recall by 15.09% and 17.47%, respectively, and in specificity by 15.80% and 14.99%, respectively. These findings suggest that the optimized models have better predictive capabilities for identifying landslide and non-landslide areas, effectively reducing the uncertainty introduced by point data in landslide risk prediction. [ABSTRACT FROM AUTHOR]

Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes. [ABSTRACT FROM AUTHOR]

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation. [ABSTRACT FROM AUTHOR]