Ten simple rules for working with high resolution remote sensing data by Mahood, Adam L., Joseph, Maxwell B., Spiers, Anna I., Koontz, Michael J., Ilangakoon, Nayani, Solvik, Kylen K., Quarderer, Nathan, McGlinchy, Joe, Scholl, Victoria M., St. Denis, Lise A., Nagy, Chelsea, Braswell, Anna, Rossi, Matthew W., Herwehe, Lauren, Wasser, Leah, Cattau, Megan E., Iglesias, Virginia, Yao, Fangfang, Leyk, Stefan, Balch, Jennifer K.

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Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

“…The RF model exhibited the highest predictive accuracy, with an R-squared value of 0.90 and a Mean Squared Error (MSE) of 0.29 on the training dataset, while achieving 0.52 R-squared and 1.37 MSE on the test dataset. …”
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Estimation of Near-Surface Ozone Concentration Across China and Its Spatiotemporal Variations During the COVID-19 Pandemic by Shikang Guan, Xiaotong Zhang, Wenbo Zhao, Yanjun Duan, Xinpei Han, Lingfeng Lv, Mengyao Li, Bo Jiang, Yunjun Yao, Shunlin Liang

“…Therefore, an improved similarity distance-based space-time random forest (SDSTRF) model was developed to estimate the near-surface O₃ concentration using the surface measurements, satellite O₃ precursors, meteorological variables, and other auxiliary information. …”
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STREAM: a system for tracing radiative transfer and energy balance in heterogeneous surfaces by Tengyuan Fan, Zunjian Bian, Jean-Louis Roujean, Huaguo Huang, Hua Li, Junhua Bai, Biao Cao, Yongming Du, Qing Xiao, Qinhuo Liu

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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…The findings demonstrated that the ensemble learning models, specifically random forest and gradient boosting decision tree regression, delivered the best overall performance, with a coefficient of determination (R2) ranging from 0.40 to 0.80 and a mean absolute error (MAE) between 0.11 and 4.36. …”
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Self SOC Estimation for Second-Life Lithium-Ion Batteries by Joelton Deonei Gotz, Emilson Ribeiro Viana, Jose Rodolfo Galvao, Fernanda Cristina Correa, Milton Borsato, Alceu Andre Badin

“…In the first phase, a Random Forest (RF) model was built and trained to discover the curve capacity of different SLB characteristics and capacities. …”
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Modelación de estructuras diamétricas con la función Log-Logistic en bosques naturales de Durango, México by Sacramento Corral-Rivas, Omar Martínez-Ruíz, Juan Abel Nájera-Luna, Friday Nwabueze Ogana, José Javier Corral-Rivas

“…Los modelos de distribuciones diamétricas son herramientas útiles para predecir el crecimiento y rendimiento de masas forestales y planear actividades de manejo forestal sustentable. El objetivo de este trabajo fue analizar la capacidad de ajuste de la función de densidad de probabilidad Log-Logistic a través de un estimador de parámetros basado en percentiles y evaluar la precisión de dos alternativas de modelización de las distribuciones diamétricas de rodales naturales del noroeste del estado de Durango. …”
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Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine by Xulong Duan, Rana Waqar Aslam, Syed Ali Asad Naqvi, Dmitry E. Kucher, Zohaib Afzal, Danish Raza, Rana Muhammad Zulqarnain, Yahia Said

“…Using Google Earth Engine for spatiotemporal fusion and random forest classification for feature optimization, we demonstrate the superiority of fused multisensor indices, with vegetation condition index and normalized vegetation-soil water index achieving the highest accuracy (<italic>r</italic> &#x003D; 0.56&#x2013;0.59) at 10&#x2013;40 cm depths, while single-sensor optical indices underperformed at 50 cm (<italic>r</italic> &lt; 0.30). …”
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Identification of Earthquake Precursors Origin and AI Framework for Automatic Classification for One of These Precursors by Ghada Ali, Lotfy Samy, Omar M. Saad, Ali G. Hafez, El-Sayed Hasaneen, Kamal AbdElrahman, Ibrahim Salah, Mohammed S. Fnais, Hamed Nofel, Ahmed M. Mohamed

“…The examined classification topologies are Logistic Regression (LR), K-nearest neighbors Classifier (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest Classifier (RF), XGB Classifier, Na&#x00EF;ve Bayes (NB), Voting Classifier and Convolutional Neural Network (CNN). …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Results indicate that (1) the proposed paradigm achieves optimal SOC content prediction accuracy in humid regions, with a root mean square error (RMSE) of 3.58 g kg−1, a coefficient of determination (R2) of 0.76, a ratio of performance to interquartile distance (RPIQ) of 2.26, and a mean absolute error (MAE) of 4.73 g kg−1. …”
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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang, Yuxin Huo

“…Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R²) of 0.8618, root mean square error (RMSE) of 0.0182 m³ m⁻³, and mean absolute error (MAE) of 0.0148 m³ m⁻³ across eight soil depths. …”
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Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration by Houda Lamane, Latifa Mouhir, Rachid Moussadek, Bouamar Baghdad, Ozgur Kisi, Ali El Bilali

“…In this regard, the current study presents a novel framework involving four standalone ML models (extra trees (ET), random forest (RF), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost)) and their combination with genetic programming (GP). …”
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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

“…In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. …”
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Alterations in tidal volume over recording time during pulmonary function testing by barometric whole-body plethysmography in client-owned cats: a multicenter retrospective investi... by Wei-Tao Chang, Laín García-Guasch, Hannah Gareis, Bianka Schulz, Yoshiki Yamaya, Pei-Ying Lo, Chin-Hao Chang, Hui-Wen Chen, Chung-Hui Lin

“…The trend of alterations in TV was not affected by site, emotional status, health status, age, or gender. Forest plots with 95% confidence intervals of TV generated from short sections, alongside conventional data averaging breaths over a 5-minute period (TV-All), showed acceptable margins of error at all sites. …”
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Effectiveness of a transfluthrin emanator and insecticide-treated barrier screen in reducing Anopheles biting in a temporary shelter in Sumatra, Indonesia by Timothy A. Burton, Lepa Syahrani, Dendi Hadi Permana, Ismail Ekoprayitno Rozi, Rifqi Risandi, Siti Zubaidah, Syarifah Zulfah, Ma’as M. Maloha, Rusli Efendi, Maria Kristiana, Puji B. S. Asih, Din Syafruddin, Neil F. Lobo

“…Collections occurred near Bukit Duabelas National Park in central Sumatra, Indonesia, an area characterized by secondary forest undergoing widespread conversion to palm and rubber plantations. …”
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A novel approach for downscaling land surface temperature from 30 m to 10 m using land features multi-interaction by Alfred Homère Ngandam Mfondoum, Sofia Hakdaoui, Ali Mihi, Ibrahima Diba, Mesmin Tchindjang, Luc Beni Moutila, Frederic Chamberlain Lounang Tchatchouang

“…Finally, the machine learning algorithm of random forest based on different seeds achieved overall accuracy between [0.92–1]. …”
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Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…Four predictive models—linear regression (LR), LASSO regression, random forest regression (RF), and support vector regression (SVR)—were developed to estimate Triton DRI-OCT measurements from Cirrus HD-OCT 5000 outputs. …”
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Development of hybrid stacking machine learning for evaluating parameters affecting refrigerated shrimp coated with chitosan-loaded Salvia officinalis nanoemulsions by Mehran Sayadi, Elahe Abedi, Najmeh Oliyaei, Maryam Mousavifard

“…It aggregates a support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) as a meta-learner. The droplet size of the EO nanoemulsion was approximately 156 nm, and FTIR spectroscopy confirmed the encapsulation of the EO. …”
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Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning by Dthenifer Cordeiro Santana, Rafael Felipe Ratke, Fabio Luiz Zanatta, Cid Naudi Silva Campos, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Natielly Pereira da Silva, Gabriela Souza Oliveira, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Matildes Blanco, Paulo Eduardo Teodoro

“…Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. …”
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A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas by Ju Wang, Ju Wang, Ju Wang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Xinming Zhu, Xinming Zhu, Xinming Zhu, Dong Fan, Dong Fan, Dong Fan, Menghua Li, Menghua Li, Menghua Li, Junyi Chen, Junyi Chen, Junyi Chen

“…Based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. …”
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