Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

“…We tested three machine learning (ML) models based on random forest (RF) models, generalised additive models (GAMs), and linear regression models (LMs). …”
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Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches by Pham Nguyen Dang Khoa, Dinh Gia Huy, Nguyen Canh Lam, Dang Hai Quoc, Pham Hoang Thai, Nguyen Quyen Tat, Tran Minh Cong

“…Indeed, five ML techniques, linear regression (LR), decision tree (DT), random forest (RF), XGBoost, and AdaBoost, were used to develop ship fuel consumption models in this study. …”
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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. …”
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Voice as a Health Indicator: The Use of Sound Analysis and AI for Monitoring Respiratory Function by Nicki Lentz-Nielsen, Lars Maaløe, Pascal Madeleine, Stig Nikolaj Blomberg

“…Despite an error range of −1252 to 1435 mL/s, most predictions fell within the LoA, indicating good performance in estimating the FEV1. …”
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AI-Powered Forecasting of Environmental Impacts and Construction Costs to Enhance Project Management in Highway Projects by Joon-Soo Kim

“…The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. …”
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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. …”
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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

“…The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. …”
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Spatiotemporal Distribution of Soil Thermal Conductivity in Chinese Loess Plateau by Yan Xu, Yibo Zhang, Wanghai Tao, Mingjiang Deng

“…The results show that the LT model is the best in the relevant evaluation indices, with a determination coefficient (<i>R</i>²) of 0.84, root mean square error (<i>RMSE</i>) of 0.18, and relative error (<i>RE</i>) of 0.16. …”
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Utility of Certain AI Models in Climate-Induced Disasters by Ritusnata Mishra, Sanjeev Kumar, Himangshu Sarkar, Chandra Shekhar Prasad Ojha

“…Out of all the models, the GBM model performed better than other AI tools in both the field of energy dissipation of stepped channels with a coefficient of determination (R²) of 0.998, root mean square error (RMSE) of 0.00182, and mean absolute error (MAE) of 0.0016 and sediment trapping efficiency of vortex tube ejector with an R² of 0.997, RMSE of 0.769, and MAE of 0.531 during testing. …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an <i>R</i>² score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.…”
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Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms by Sherly Ardhya Garini, Ary Mazharuddin Shiddiqi, Widya Utama, Alif Nurdien Fitrah Insani

“…Results indicated that XGBoost was the most efficient and accurate, especially for RHOB, NPHI, DTCO, and DTSM, with the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. …”
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Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model by Xianmei Zhou, Shanliang Zhu, Wentao Jia, Hengkai Yao

“…In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R²) were employed to evaluate the model’s performance. …”
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Study of Spatial and Temporal Characteristics and Influencing Factors of Net Carbon Emissions in Hubei Province Based on Interpretable Machine Learning by Junyi Zhao, Bingyao Jia, Jing Wu, Xiaolu Wu

“…This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. …”
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Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning by Yongjie Ma, Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan, Yanjun Zhang

“…Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). …”
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Enhanced soil organic carbon mapping in Gannan’s alpine meadows: A comparative analysis of machine learning models and satellite data by Xingyu Liu, Meiling Zhang, Ziming Ma

“…The performance of Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Descent Boosted Regression Tree (GBDT) models was evaluated using metrics such as the determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), and Lin’s concordance correlation coefficient (LCCC). …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

“…Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. …”
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Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments by Montaser Abdelsattar, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Using a dataset of 5,000 samples (80% for training, 20% for testing), the models—Support Vector Regression (SVR), Lasso Regression, Ridge Regression (RR), Linear Regression (LR), AdaBoost, Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—were evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). …”
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Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA by Birhan Getachew Tikuye, Ram Lakhan Ray

“…The RF model demonstrated the best performance in model testing, with the lowest root mean square error (RMSE = 4.17) and mean absolute error (MAE = 3), as well as the highest coefficient of determination (R2 = 0.78). …”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

“…The comprehensive experimental results show that the predictive performance of IFTformer is superior to that of baseline models, with a normalised root mean square error (NRMSE) of 3.64% and a normalised mean absolute error (NMAE) of 2.44%. …”
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Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors by Mehmet Taştan

“…Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). …”
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