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The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. [ABSTRACT FROM AUTHOR]

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks. [ABSTRACT FROM AUTHOR]

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers. [ABSTRACT FROM AUTHOR]

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method's capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. [ABSTRACT FROM AUTHOR]

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor's contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors' adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. [ABSTRACT FROM AUTHOR]

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed's growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features. [ABSTRACT FROM AUTHOR]

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R2 = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used. [ABSTRACT FROM AUTHOR]

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay's shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds' shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales. [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]

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a 'LiDAR AGB model', to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R2 = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology. [ABSTRACT FROM AUTHOR]

Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture. [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]

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset. [ABSTRACT FROM AUTHOR]

Maize plays a crucial role in China's grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha−2) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran's I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran's I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to evaluate the spatiotemporal trends of soil erosion, to reveal the contributions of driving factors, to elucidate the impacts of ecological restoration policies, and to provide a scientific basis for the management of soil erosion on the Tibetan Plateau. [Methods] This study assessed the temporal and spatial characteristics of soil erosion by water on the Tibetan Plateau from 1990 to 2020, and identified the contribution of climatic, anthropogenic and topographic factors to soil erosion by Random Forest Regression. [Results] (1) The average soil erosion rate on the Tibetan Plateau was 305.78 t/km², and more than 80% of the area was slightly erosion during 1990-2020. Soil erosion decreased before 2010 and increased after 2010, showing a nonlinear variation. (2) Climate factors had a greater impact on soil erosion than anthropogenic factors in the Tibetan Plateau, and annual precipitation was the main factor on soil erosion. In addition, since 2010, the influence of anthropogenic factors on soil erosion gradually increased. [Conclusion] Climate factors are the primary driving force inducing soil erosion in the Tibetan Plateau. The ecological restoration policies implemented in the Tibetan Plateau have initially achieved results, but the impact of climate change on soil erosion is more significant. Additionally, Random Forest Regression is effective for exploring the driving factors on soil erosion. [ABSTRACT FROM AUTHOR]

(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately. [ABSTRACT FROM AUTHOR]

This study introduces an innovative scheme for classifying uncrewed aerial vehicle (UAV)-derived vehicle trajectory behaviors by employing machine learning (ML) techniques to transform original trajectories into various sequences: space–time, speed–time, and azimuth–time. These transformed sequences were subjected to normalization for uniform data analysis, facilitating the classification of trajectories into six distinct categories through the application of three ML classifiers: random forest, time series forest (TSF), and canonical time series characteristics. Testing was performed across three different intersections to reveal an accuracy exceeding 90%, underlining the superior performance of integrating azimuth–time and speed–time sequences over conventional space–time sequences for analyzing trajectory behaviors. This research highlights the TSF classifier's robustness when incorporating speed data, demonstrating its efficiency in feature extraction and reliability in intricate trajectory pattern handling. This study's results indicate that integrating direction and speed information significantly enhances predictive accuracy and model robustness. This comprehensive approach, which leverages UAV-derived trajectories and advanced ML techniques, represents a significant step forward in understanding vehicle trajectory behaviors, aligning with the goals of enhancing traffic control and management strategies for better urban mobility. [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]

In this study, we propose an innovative concept that applies an AI-based approach using the random forest algorithm integrated with fractal and discrete wavelet transform features of nuclear chromatin. This strategy could be employed to identify subtle structural changes in cells that are in the early stages of programmed cell death. The code for the random forest model is developed using the Scikit-learn library in Python and includes hyperparameter tuning and cross-validation to optimize performance. The suggested input data for the model are chromatin fractal dimension, fractal lacunarity, and three wavelet coefficient energies obtained through high-pass and low-pass filtering. Additionally, the code contains several methods to assess the performance metrics of the model. This model holds potential as a starting point for designing simple yet advanced AI biosensors capable of detecting apoptotic cells that are not discernible through conventional microscopy techniques. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state estimation of the AC bus voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models to assess the effectiveness of DeepFWX. Key performance indicators (KPIs) such as MAE, MAPE, RMSE, RMSPE, and R2 were considered. Superior performance across all the evaluated metrics was demonstrated by DeepFWX, as it achieved MAE of [15.03, 0.58], MAPE of [0.09, 0.14], RMSE of [70.39, 5.64], RMSPE of [0.34, 0.85], as well as the R2 of [0.99, 0.99] for the systems states [X1, X2]. The proposed hybrid approach anticipates the capabilities of FO modeling, predictive control, and random forest intelligent algorithms to achieve the precise control of AC bus voltage, thereby enhancing the overall stability and performance of OWTs in the evolving sector of renewable energy integration. [ABSTRACT FROM AUTHOR]

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study aims to demonstrate and quantify the usefulness of three different AI models for lymph node involvement prediction in prostate cancer patients comparing the performance with current routinely used clinical nomograms. Knowing the lymph node involvement with accuracy and confidence can influence the type of surgical and therapeutic choice. PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction. [ABSTRACT FROM AUTHOR]

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model's performance. [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]

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi'an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi'an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi'an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi'an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper. [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]

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development. [ABSTRACT FROM AUTHOR]

One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study investigated the relationship between vertical ballast stiffness and the track longitudinal level. Initially, the ballast stiffness and track longitudinal level data were acquired through a series of experimental measurements conducted on a reference test track along the Tehran–Mashhad railway line, utilizing recording cars for geometric track and stiffness recordings. Subsequently, the correlation between the track longitudinal level and ballast stiffness was surveyed using both frequency-based techniques and machine learning (ML) algorithms. The power spectrum density (PSD) as a frequency-based technique was employed, alongside ML algorithms, including linear regression, decision trees, and random forests, for correlation mining analyses. The results showed a robust and statistically significant relationship between the vertical ballast stiffness and longitudinal levels of railway tracks. Specifically, the PSD data exhibited a considerable correlation, especially within the 1–4 rad/m wave number range. Furthermore, the data analyses conducted using ML methods indicated that the values of the root mean square error (RMSE) were about 0.05, 0.07, and 0.06 for the linear regression, decision tree, and random forest algorithms, respectively, demonstrating the adequate accuracy of ML-based approaches. [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]

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data. [ABSTRACT FROM AUTHOR]

This study focuses on the acoustic classification of delphinid species at the southern continental slope of Brazil. Recordings were collected between 2013 and 2015 using towed arrays and were processed using a classifier to identify the species in the recordings. Using Raven Pro 1.6 software (Cornell Laboratory of Ornithology, Ithaca, NY), we analyzed whistles for species identification. The random forest algorithm in R facilitates classification analysis based on acoustic parameters, including low, high, delta, center, beginning, and ending frequencies, and duration. Evaluation metrics, such as correct and incorrect classification percentages, global accuracy, balanced accuracy, and p-values, were employed. Receiver operating characteristic curves and area-under-the-curve (AUC) values demonstrated well-fitting models (AUC ≥ 0.7) for species definition. Duration and delta frequency emerged as crucial parameters for classification, as indicated by the decrease in mean accuracy. Multivariate dispersion plots visualized the proximity between acoustic and visual match data and exclusively acoustic encounter (EAE) data. The EAE results classified as Delphinus delphis (n = 6), Stenella frontalis (n = 3), and Stenella longirostris (n = 2) provide valuable insights into the presence of these species between approximately 23° and 34° S in Brazil. This study demonstrates the effectiveness of acousting classification in discriminating delphinids through whistle parameters. [ABSTRACT FROM AUTHOR]

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research. [ABSTRACT FROM AUTHOR]

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available. [ABSTRACT FROM AUTHOR]

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity (MDI) method. To establish productivity prediction models, linear regression (LR), random forest regression (RF), support vector regression (SVR), backpropagation (BP), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms are used. Their performance is evaluated using the coefficient of determination (R²), explained variance score (EV), mean squared error (MSE), and mean absolute error (MAE) metrics. The LightGBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query, oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development. [ABSTRACT FROM AUTHOR]

Accurate forecasting of the temporal distribution pattern of sub-daily precipitation is of paramount importance for effective flood control design and early warning systems. This study focuses on improving the accuracy of such forecasts by employing post-processing techniques. The European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation product over Iran was adopted along with three post-processing methods including Quantile Mapping (QM), Support Vector Machine (SVM), and Random Forest (RF). The accuracy of the forecasts for various precipitation temporal characteristics, including the start, duration, and end of precipitation events were evaluated. The RF method proved to be the most effective in improving forecast accuracy, especially in regions with higher precipitation rates. Additionally, RF corrected the first quartile of precipitation forecasts across all precipitation regions, significantly enhancing forecast accuracy in regions 3 and 5 of Iran. As for the temporal distribution pattern, post-processing methods improved the accuracy of the forecasts across all regions. The QM method performed better in terms of distributing precipitation amounts among quartiles. Moreover, all post-processing methods showed a high degree of similarity between observed and forecasted temporal distribution patterns. The deterministic evaluation showed that RF outperforms other methods in enhancing the accuracy of most precipitation quartiles, particularly that of the third quartile. The SVM and QM methods showed mixed performances, improving accuracy in some quartiles but performing adversely in others. Overall, this research highlighted the importance of data post-processing in enhancing the accuracy of precipitation forecasts and their temporal distribution patterns. The RF method proved to be the most effective post-processing technique. These findings have significant implications for flood forecasting and management in regions prone to extreme precipitation events. [ABSTRACT FROM AUTHOR]

Urban expansion has made thermal conditions a significant concern in the city of Oran. The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. With a mean temperature of 37.27 °C, the simulation for 2025 shows that temperatures are expected to decrease by 0.53 °C, with forests and water surfaces being the main classes. Our findings provide valuable information on the future thermal balance of cities and can assist planners in designing more effective medium and long-term policies from both environmental and tourism perspectives. [ABSTRACT FROM AUTHOR]

Cloud cover, the percentage of the sky obscured by clouds, has significant effects on agriculture, global warming, and solar energy. This research has investigated machine learning techniques to accurately predict cloud cover in Bangladesh, comparing the effectiveness of various algorithms such as k-nearest neighbors, decision trees, Naïve Bayes, support vector machine, logistic regression, gradient boosting machine, and random forest using data from the Bangladesh Meteorological Department from 1980 to 2022. Validation is conducted using confusion matrix analysis, which rigorously assesses validity and identifies errors. The study presents a methodology for using training and test sets to objectively estimate performance metrics, including precision, sensitivity, accuracy, positive and negative predictive values, F 1 score, and Cohen's kappa coefficient. Results are based on the analysis of training and test data sets. The mentioned machine learning techniques utilize cross-validation. Random forest shows the highest performance. Cross-validation adjusts the results, confirming the random forest's top accuracy, kappa value, and F 1 score. Next in order, the support vector machine performs well. Among decision tree algorithms, it was discovered that the CART model performed better than C5.0. The Naïve Bayes algorithm performs the least well. [ABSTRACT FROM AUTHOR]

Air temperature holds significant importance in microclimate and environmental health studies, playing a crucial role in weather regulation. There is a need to develop a reliable model capable of accurately capturing air temperature variations. In this study, we focused on the Amazon-Cerrado transitional forest, constructing a robust predictive model for hourly temperature fluctuations. This forest, situated approximately 50 km northwest of Sinop, Mato Grosso, Brazil, is a transitional area, making it important to investigate its climatic behavior and ecosystems. We estimated air temperature using machine learning techniques such as Random Forest, Gradient Boosting, Multilayer Perceptron, and Support Vector Regressor, aiming to evaluate the most effective models based on relevant metrics. Performance assessments were conducted during both dry and rainy seasons to verify their adaptability. The top-performing Random Forest model demonstrated Willmott and Spearman indexes above 0.97. The air relative humidity, solar radiation, and volumetric soil water content were identified as the most important features, evaluated with Willmott and Spearman indexes above 0.95 in a model with such dimensionality reduction. These results underscore the efficacy of machine learning techniques in accurately estimating air temperature. [ABSTRACT FROM AUTHOR]

Access to high-resolution historical climate data is vital for both theoretical and applied climatology. The aim of this paper is to propose a hybrid algorithm, which consists of two main steps. At the first step, using temperature lapse rate, the temperature data of ERA5, MERRA2 and CFS were downscaled. The temperature lapse rate was calculated using MODIS temperature data. Then, at the second step, the ability of machine learning algorithms was examined in generating daily 2m temperature data with spatial resolution of 1km for Iran during 1980-2021. The inputs of the machine learning algorithms include the downscaled outputs of ERA5, MERRA2 and CFS data (which were calculated at the first step), elevation, aspect and Julian day. Several machine learning algorithms were examined including multi-layered perceptron neural networks (MLPNN), Random Forest (RF), Gradient Boosting (GB), Adaptive Boosting (AB) and Cubist Regression (CB). The results indicated that the MLPNN outperforms other techniques. Also, the final temperature map was depicted for Feb and Jul and also the MLPNN output predictions were evaluated for six different climate regions in the country. Evaluation results in regional section indicated that the MLPNN best performance was in very warm region with low rainfall while its worst performance was in mountainous and cold region. [ABSTRACT FROM AUTHOR]

Electrolytic hydrogen production from water is a promising approach for obtaining clean energy. The development of efficient oxygen evolution reaction (OER) electrocatalysts is crucial for the generation of hydrogen through water electrolysis. Transition metal oxides, such as Fe, Co, and Mn, have shown potential as efficient OER electrocatalysts for water splitting. However, accurately predicting their electrocatalytic performance in complex compositional spaces remains a challenge, impeding the precise design of compositions and processes for optimal performance. Herein, a machine learning-based method is proposed for predicting the OER activity of (FeCoMn)Ox catalysts across a wide range of compositions. Physical features that are highly relevant to the OER overpotential (OP) are identified and analyzed. The random forest algorithm is successfully used to establish the relationship between composition and overpotential. The model demonstrates good accuracy in predicting the outcomes of new experiments, with a mean relative error (MRE) of 9.3%. The features based on covalent radius (RC) and the number of electrons in the outermost d orbitals (DEs) are the primary factors. Their variances (δRC and δDE) exhibit a linearly decreasing relationship with the overpotential (OP), providing direct guidance for designing OP-oriented components. This work presents an effective and innovative approach for predicting and analyzing the physical factors of transition metal oxide electrocatalysts, which can enhance the design of highly catalytic materials for electrocatalysis. [ABSTRACT FROM AUTHOR]

Aim: Seamounts are conspicuous geological features with an important ecological role and can be considered vulnerable marine ecosystems (VMEs). Since many deep‐sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold‐water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data are scarce. Location: Seamounts around the Cabo Verde archipelago (NW Africa). Methods: We used species presence–absence data obtained from remotely operated vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100‐m‐resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (generalized additive model and random forest) were employed and single‐model predictions were combined into a final weighted‐average ensemble model. Model performance was validated using different metrics through cross‐validation. Results: Terrain orientation, at broad scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic–topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys. Main Conclusions: Our study shows how data‐poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning. [ABSTRACT FROM AUTHOR]

Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models and understanding the variables influencing their outcomes are essential. This study developed models that integrate wavelet transformation (WT) with advanced artificial intelligence (AI) models, increasing prediction accuracy. This study investigates the prediction of meteorological droughts using standalone bootstrapped random forest (BRF) and bi-directional long short-term memory (Bi-LSTM) models, compared to wavelet-decomposed hybrid models (WBRF, WBi-LSTM). These models were evaluated in the Mun River Basin, Thailand, utilizing monthly meteorological data (1993–2022) from the Thai Meteorological Department. The predictions were assessed using statistical metrics (R2, MAE, RMSE, and MAPE). For the Standardized Precipitation Index (SPI), the hybrid WBRF model consistently outperformed the standalone BRF across various metrics and timescales, demonstrating higher R2 (0.89–0.97 for SPI-3) and lower error metrics (MAE: 0.144–0.21 for SPI-6, RMSE: 0.2–0.3 for SPI-12). Similarly, the hybrid WBi-LSTM model outperformed the standalone Bi-LSTM in SPI predictions, exhibiting higher R2 (0.87–0.91 for SPI-3) and lower error metrics (MAE: 0.19–0.23 for SPI-6, RMSE: 0.27–0.81 for SPI-12) across all timescales. This trend was also observed for the China Z-index, Modified China Z-index, Hutchinson Drought Severity Index, and Rainfall Anomaly Index, where hybrid models achieved superior performance compared to standalone models. The WBi-LSTM model emerged as the preferred choice across different timespans. The integration of WT enhanced the predictive accuracy of hybrid models, making them effective tools for drought prediction. [ABSTRACT FROM AUTHOR]

Aim: Tidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10‐m resolution. Location: Global. Time period: 2020. Major taxa studied: Tidal marshes. Methods: To map the location of the world's tidal marshes at 10‐m resolution, we applied a random forest classification model to Earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60° N and 60° S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.85. Results: We estimate the global extent of tidal marshes in 2020 to be 52,880 km2 (95% CI: 32,030 to 59,780 km2) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km2; CI: 11,200–20,900) occurs within the USA. Main conclusions: Our analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the world's coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the world's coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services. [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]

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the "black box" scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary: Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the "black box" reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points: Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processesThe proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemesThe partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method [ABSTRACT FROM AUTHOR]

The fast growth in renewable power generation, crucial for reducing carbon emissions in the traditional energy system, is constrained by negative environmental and economic repercussions, demanding a smarter integration with conventional energy sources. However, the seamless integration of renewable energy into grid imposes major challenges due to the inherent environment dependence of energy production, necessitating proactive forecasts to anticipate fluctuations in efficiency and dependability that might have an influence on the overall grid status and living style of the users. The objective of the work is to add a consistent dataset in the machine learning community and a new ensemble to deal with the problems pertaining to the projection and integration of renewable energy using predictive modeling. The model includes a stacked ensemble of Random Forest and XGBoost algorithms. The proposed ensemble has been successfully applied to the dataset of Agartala City. In this model, the Random Forest model is used to forecast the target variable based on input parameters, followed by the use of the XGBoost model to improve predictions through a combination of Random Forest predictions, with the goal of leveraging the strengths of multiple models while mitigating their weaknesses. The meta-model, a basic logistic regression, then learns the best combination of these predictions, allowing for the maximum potential accuracy. The model has been evaluated on R2 and RMSE. The accuracy of 99% reveals its feasibility and superiority both in short-term and long-term predictions. [ABSTRACT FROM AUTHOR]

The automatic categorization of power quality disturbances (PQDs) presents a formidable task for utility and industry professionals. This study introduces a novel technique for the automatic classification of PQDs that involves employing an ensemble classification model combined with Tunable-Q wavelet transform (TQWT) analysis. The TQWT has been observed to accurately capture the sparsity in PQD signals within the time-scale domain. The methodology under consideration commences by implementing the TQWT to effectively assess the behavior and sparsity of PQD signals. The considered signals are decomposed through the use of TQWT into a set of sub-bands that possess limited bandwidth. This decomposition process enables enhanced feature extraction capabilities. In order to assess the efficacy of classification algorithms, a set of three distinct statistical features (variance, skewness, and kurtosis) is obtained from the TQWT decomposed signals, which are utilized as the training vector. The present study employs random forest and AdaBoost algorithm-based classifiers to effectively classify twelve frequently occurring PQD signals. An investigation was conducted regarding several power signal disturbances, each associated with varying levels of noise. The objective was to demonstrate the effectiveness of a proposed classification scheme when operating under noisy conditions. A comparative analysis of the classification accuracies using previously proposed techniques reveals a distinctly enhanced performance. The research results illustrate that the proposed ensemble classification model exhibits exceptional performance in terms of classification accuracy, achieving a perfect score of 100%. These findings further highlight the superior recognition performance of the random forest classifier within the ensemble. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent analysis of GPR data and overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model was constructed for regressing the grouting thickness based on GPR waveforms. A full-scale model test and corresponding numerical simulations were carried out to collect GPR data at 400 and 900 MHz, with known backfill grouting thickness. The model test helps address the limitation of not knowing the grout body condition in actual field detection. The data were then used to create machine learning datasets. The method of feature selection was proposed based on the analysis of feature importance and the electromagnetic (EM) propagation law in mediums. The research shows that: (1) the CatBoost & BO-TPE model exhibited outstanding performance in both experimental and numerical data, achieving R2 values of 0.9760, 0.8971, 0.8808, and 0.5437 for numerical data and test data at 400 and 900 MHz. It outperformed extreme gradient boosting (XGBoost) and random forest (RF) in terms of performance in the backfill grouting thickness regression; (2) compared with the full-waveform GPR data, the feature selection method proposed in this paper can promote the performance of the model. The selected features within the 5-30 ns of the A-scan can yield the best performance for the model; (3) compared to GPR data at 900 MHz, GPR data at 400 MHz exhibited better performance in the CatBoost & BO-TPE model. This indicates that the results of the machine learning model can provide feedback for the selection of GPR parameters; (4) the application results of the trained CatBoost & BO-TPE model in engineering are in line with the patterns observed through traditional processing methods, yet they demonstrate a more quantitative and objective nature compared to the traditional method. [ABSTRACT FROM AUTHOR]

This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network’s ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Forest (RF) and Support Vector Machine (SVM) algorithms to predict scalability. A large dataset of 40,000 data points was collected, focusing on six key metrics: Response Time, Bandwidth, Latency, Error Rate, Throughput, and Number of Users Connected. The data was preprocessed and divided into training and testing sets (80:20 ratio). Both RF and SVM algorithms were trained on the dataset, and a comparative analysis was conducted to determine which algorithm performed better. The results showed that the RF model outperformed the SVM model, achieving an accuracy of 95.0% compared to 91.0%. The RF model also exhibited higher precision, recall, and AUC scores. Feature importance analysis revealed that Response Time and Throughput were the most significant factors in determining network scalability. The study demonstrated the effectiveness of the RF model in predicting broadband network scalability, with a lower loss value (0.0133 for training and 0.0160 for validation) compared to the SVM model. This approach will help network operators and administrators predict and improve network scalability, ensuring reliable and fast internet connectivity. The study contributes to the development of machine learning-based solutions for broadband network performance evaluation and optimization. [ABSTRACT FROM AUTHOR]