Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…Notably, all ML models outperformed empirical models, with clustering of weather stations further reducing prediction error in ET0 estimation by 10 to 18 %. These findings demonstrate the potential of ML models for accurate ET0 estimation with limited data, supporting agricultural water management and enhancing resilience in water-stressed areas.…”
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Multi-Airport Capacity Decoupling Analysis Using Hybrid and Integrated Surface–Airspace Traffic Modeling by Lei Yang, Yilong Wang, Sichen Liu, Mengfei Wang, Shuce Wang, Yumeng Ren

“…In the surface model, we utilize linear regression and random forest regression to model unimpeded taxiing time and taxiway network delays due to sparsity of ground traffic. …”
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Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems by Nail Beisekenov, Wiyao Banakinaou, Ayomikun David Ajayi, Hideo Hasegawa, Aoda Tadao

“…The eXtreme Gradient Boosting (XGBoost) model achieved the highest accuracy, with a cross-validation coefficient of determination (R²) of 0.88, a test R² of 0.91, and a root mean square error (RMSE) of 0.17 t of carbon per hectare (t C ha⁻¹). …”
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Polyomaviruses and the risk of oral cancer: a systematic review and meta-analysis by Tahoora Mousavi, Fatemeh Shokoohy, Mahmood Moosazadeh

“…Data analysis was performed using STATA Ver. 11 software, and the standard error was calculated using the binomial distribution formula. …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…We achieved a mean square error (MSE) value of 1.5 using Morgan-RDKit-PubChem-Conjoint descriptors and 1D-CNN on the PDAC dataset. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…The results indicated that CatBoost outperformed the other models, achieving mean absolute relative error (MARE) values of 4.95% for the entire dataset, 5.92% for the testing subset, and 4.71% for the training subset. …”
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Prediction of Electrotactile Stimulus Threshold in Real Time Using Voltage Waveforms Between Electrodes by Vibol Yem, Yasushi Ikei, Hiroyuki Kajimoto

“…Second, we applied Random Forest regression using R and C related data as inputs. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…The sampling scenarios were not significantly different, based on root mean square error (F = 1.7; <i>p</i>-value = 0.15) and mean absolute error (F = 0.4; <i>p</i>-value = 0.79); however, significant differences were observed in the coefficient of determination (F = 41.2; <i>p</i>-value < 0.00) across all models. …”
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SNUH methylation classifier for CNS tumors by Kwanghoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae-Kyung Won, Kwangsoo Kim, Chul-Kee Park, Sung-Hye Park

“…Results Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. …”
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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. The support vector regression method in this study has a lower error value, namely 0.91 for MAE and 1.30 for MSE. …”
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Assessing evapotranspiration dynamics across central Europe in the context of land–atmosphere drivers by A. Fluhrer, A. Fluhrer, M. J. Baur, M. Piles, B. Bayat, M. Rahmati, D. Chaparro, D. Chaparro, C. Dubois, C. Dubois, F. M. Hellwig, F. M. Hellwig, C. Montzka, A. Kübert, M. M. Mueller, M. M. Mueller, I. Augscheller, F. Jonard, K. Schellenberg, K. Schellenberg, T. Jagdhuber, T. Jagdhuber

“…The land cover at the selected ICOS stations ranged from deciduous broad-leaf forests, evergreen needle-leaf forests, and mixed forests to agriculture. …”
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Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment by Yu-Qi Wang, Wenchong Tian, Hao-Lin Yang, Yun-Peng Song, Jia-Ji Chen, Qiong-Ying Xu, Wan-Xin Yin, Le-Qi Ding, Xi-Qi Li, Han-Tao Wang, Ai-Jie Wang, Hong-Cheng Wang

“…Leveraging multi-stage water quality parameters, we developed a machine learning (ML) framework for coagulation control that incorporates knowledge embedding (KE) through hyper-parametric constraints on threshold water quality, energy consumption, and economic costs. Random forest (RF) has the best performance among the eight methods with a percentage error of 2.53% and a coefficient of determination of 0.9922. …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…Results For this mock malaria prediction study, the balanced error rate on a test dataset not used for model training (208 samples) was 50% for sPLSDA + SVMs and 50% for random forests on the smallest training dataset evaluated (20 samples) and 14% for sPLSDA + SVMs and 22% for random forests on the largest training dataset evaluated (835 samples). …”
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Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data by Nandhini Santhanam, Hee E. Kim, David Rügamer, Andreas Bender, Stefan Muthers, Chang Gyu Cho, Angelika Alonso, Kristina Szabo, Franz-Simon Centner, Holger Wenz, Thomas Ganslandt, Michael Platten, Christoph Groden, Michael Neumaier, Fabian Siegel, Máté E. Maros

“…XGB performed best (mean absolute error: 1.21 cases/day). Maximum air pressure was the top predictor, with temperature exhibiting a bimodal link. …”
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A novel framework for assessing shrublines and their geophysical constraints in alpine regions through probabilistic vegetation mapping and seed-filling algorithm by Zexi Ren, Lin Zhang, Qianlong Wang, Wanjun Hu, Zhou Shi

“…Validation against high-resolution Google Earth imagery demonstrated a high accuracy, with a mean absolute error (MAE) of 5.2227 m and R2 of 0.9935. The results indicated that the average elevation of alpine shrublines was about 4,899 m, ranging from 4,421 m to 5,163 m. …”
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Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat by Yu Han, Jiaxue Zhang, Yan Bai, Zihao Liang, Xinhui Guo, Yu Zhao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Wude Yang, Guangxin Li, Sha Yang, Xingxing Qiao, Chao Wang

“…Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. …”
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Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA by Maggie McCarter, Stella C. W. Self, Huixuan Li, Joseph A. Ewing, Lídia Gual-Gonzalez, Mufaro Kanyangarara, Melissa S. Nolan

“…Model prediction error was low, less than 2%, indicating high accuracy in predicting annual LACV human incidence at the county level. …”
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A machine learning approach to predict phyllosphere resistome abundance across urbanization gradients by Rui-Ao Ma, Yi-Hui Ding, Shifa Zhong, Ting-Ting Jing, Xuechu Chen, Si-Yu Zhang

“…Among the five tested algorithms tested in the machine learning models (ridge regression, K-nearest neighbor, support vector machine, and neural network), the random forest algorithm achieved the highest accuracy with the lowest root mean square error (27.24 vs. 40.79–46.79 for the other models). …”
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Research on Monitoring Oceanic Precipitable Water Vapor and Short-Term Rainfall Forecasting Using Low-Cost Global Navigation Satellite System Buoy by Maosheng Zhou, Pengcheng Wang, Zelu Ji, Yunzhou Li, Dingfeng Yu, Zengzhou Hao, Min Li, Delu Pan

“…Furthermore, a Random Forest model based on multiple meteorological parameters optimizes precipitation forecasting, especially in reducing false alarms. …”
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