Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields by Zitian Gao, Danlu Guo, Dongryeol Ryu, Andrew W. Western

“…We trained remote sensing (RS)-based machine learning (ML) models – Random Forest Regression (RFR), Gradient Boosting Regression (GBR) and Support Vector Regression (SVR) – to predict yields for over 400 cotton fields with ground-truth yield data. …”
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Development and Validation of a Nomogram to Predict Ventricular Fibrillation During Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction by Ruifeng Liu, Xiangyu Gao, Jihong Fan, Huiqiang Zhao

“…Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, elastic net regression, and random forest. Independent predictors were identified through multivariable logistic regression, and a nomogram was developed and validated to predict VF risk. …”
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A Learning-Based Dual-Scale Enhanced Confidence for DSM Fusion in 3-D Reconstruction of Multiview Satellite Images by Shuting Yang, Hao Chen, Fachuan He, Wen Chen, Ting Chen, Jianjun He

“…Then, a guided regularized random forest regressor is employed to identify influential confidence measures and establish their correlation with reconstruction accuracy, leading to the estimation of enhanced confidence. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…The models selected are SVR (Support Vector Regressor), DT (Decision Tree), and RFR (Random Forest Regressor) due to their wide use in the literature; therefore, the goal is to establish which one offers the best performance for this case study based on a comparative analysis using performance metrics. …”
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Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model by Abhishek Kumar Dubey, Prakash Kumar Jha, Kumari Shubha, RN Singh, Manisha Tamta, Sonam Sah, Santosh Kumar, Sanjeev Kumar, Rakesh Kumar, Kirti Saurabh, Rajeev Kumar, Anup Das, P. V. V. Prasad, Arbind Kumar Choudhary

“…Model performances was evaluated using R2, d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. …”
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UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…Maize growth inversion models were established using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF), with model efficacy compared using performance metrics. …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…The performance of the ANN model was evaluated against conventional regression models using key metrics: mean squared error (MSE), coefficient of determination (R2), and mean absolute error (MAE). …”
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Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

“…This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET₀ across these diverse climatic conditions. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning by Zhixin Wang, Zhenqi Zhang, Hailong Li, Hong Jiang, Lifei Zhuo, Huiwen Cai, Chao Chen, Sheng Zhao

“…The results indicated that the random forest model could reliably predict Chl-a, phosphate, and DIN concentrations in the MMSPA. …”
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Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Md Saifuzzaman, Rami Albasha, Maxime Leduc

“…The results also indicated that alfalfa stem density can be estimated with an error of ∼ ±6-9 stems/foot2 (1 foot = 30.48 cm) using ML regression models. …”
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Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. …”
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MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images by Angel Luis Perales Gomez, Juan Jesus Losada del Olmo, Pedro E. Lopez-de-Teruel, Alberto Ruiz, Felix J. Garcia Clemente, Andres Conesa Bueno

“…In particular, the best-performing model was Random Forest that achieved a Mean Square Error of 1.6111 and a corresponding error rate of 7.9944% on the test set.…”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…The application of GWO for hyperparameter tuning has resulted in a 37.3% reduction in root mean square error (RMSE), a 37.4% drop in mean absolute percentage error (MAPE), and a 2.06% improvement in <inline-formula> <tex-math notation="LaTeX">$\text {R}^{2}$ </tex-math></inline-formula> to improve the precision of prediction. …”
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Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors by B. Kumaravel, A. L. Amutha, T. P. Milintha Mary, Aryan Agrawal, Akshat Singh, S. Saran, Nagamaniammai Govindarajan

“…The model’s performance was evaluated using standard metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). …”
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Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale by Mahsa Bozorgi, Jordi Cristóbal, Magí Pàmies-Sans

“…., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). …”
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Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant by Tawsif Mahmud, Jiaul Haque Saboj, Preetom Nag, Goutam Saha, Bijan K. Saha

“…This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. …”
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An optimized ensemble ML-WQI model for reliable water quality prediction by minimizing the eclipsing and ambiguity issues by Ashifur Rahman, M. M. Mahbubul Syeed, Md. Rajaul Karim, Kaniz Fatema, Razib Hayat Khan, Mohammad Faisal Uddin

“…To evaluate performance, mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared ( $$R^2$$ R 2 ), fivefold cross-validation, and a comparative evaluation with existing ML models are carried out. …”
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Quantifying links between aerosol optical depth and rapid urbanisation induced land use changes, Hangzhou, China, 2000–2020 by Zhifeng Yu, Zheyu Chen, Kun Sun, Bing Qi, Ben Wang, Haijian Liu, C.K. Shum, Wenshuang Guo, Huiyan Xu, Xiaohong Yuan, Bin Zhou

“…The regional study results show that the correlation between the operational AOD observations (MOD04_3K) at 3 km resolution and the in situ AOD measurements available in Hangzhou ranges from 0.48 to 0.80, and their root mean square error (RMSE) < 0.30. During the past two decades, Hangzhou has been in the stage of rapid urbanisation, and the area of construction land has increased significantly, mainly converted from cultivated land and forests. …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

“…The study evaluated the performance of five methods for predicting the leaf area of sweet potato cultivars, including simple linear regression, artificial neural networks, support vector regression, adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The coefficient of determination (R2), relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE) were used as criteria for choosing the best methods. …”
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