A multi-model approach for estimation of ash yield in coal using Fourier transform infrared spectroscopy by Sameeksha Mishra, Anup K. Prasad, Arya Vinod, Anubhav Shukla, Shailayee Mukherjee, Bitan Purkait, Atul K. Varma, Bhabesh C. Sarkar

“…This method outperforms individual models with a coefficient of determination – R-squared (R2) of 0.883, Root Mean Square Error (RMSE) of 3.059 wt%, RMSE in percentage (RMSE%) of 30.080, Mean Bias Error in percentage (MBE%) of 3.694, and Mean Absolute Error (MAE) of 2.249 wt%. …”
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A Comparative Analysis of Different Algorithms for Estimating Evapotranspiration with Limited Observation Variables: A Case Study in Beijing, China by Di Sun, Hang Zhang, Yanbing Qi, Yanmin Ren, Zhengxian Zhang, Xuemin Li, Yuping Lv, Minghan Cheng

“…The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. …”
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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

“…Poisson regression, boosted generalized additive models, support vector machines, random forest, and extreme gradient boosting (XGB) were evaluated within a time-stratified nested cross-validation framework. …”
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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

“…In this study, we proposed a novel framework to map alpine shrublines in Xizang Rezhen National Forest Park in 2020 using multi-source spatial data, probabilistic vegetation mapping, and seed-filling algorithm. …”
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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

“…Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. …”
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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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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

“…In the same way, several experiments were conducted with a fixed value of C and ∂ kernel specific parameters in order to determine an appropriate value of error-insensitive zone (∋).Similarly, for the random forest classifier (RFC) model, the number of variables used (m) and the number of trees to be grown (k) are two user-defined parameters. …”
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Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery by Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding

“…The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. …”
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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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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…In addition, two feature selection methods, namely Mutual Information (MI) and Random Forest (RF), were employed to identify the most significant factors. …”
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Assessment of water fluxes under the dual threat of changes in land cover and climate variability in the Brazilian Cerrado biome by Dimaghi Schwamback, Abderraman R. Amorim Brandão, Ronny Berndtsson, Edson Wendland, Magnus Persson

“…The study included (i) calibration and validation of the Hydrus model through observations of soil moisture in experimental plots covered with sugarcane and pasture compared to natural forest during six years and (ii) hydrological predictions by combining Hydrus with projections from climate models (10 CMIP6 models under SSP2–4.5 and SSP5–8.5 scenarios). …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…We also assessed the predictability of global mixing ellipses using machine learning algorithms, including Spatial Transformer Networks (STN), Convolutional Neural Network (CNN) and Random Forest (RF), with mean-flow and eddy- properties as features. …”
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

“…Three ML models—Lasso regression, Random Forest, and Support Vector Regression (SVR)—were trained on a subset of 240 subjects, while 61 subjects were used for testing. …”
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Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques by Momotaz Begum, Mehedi Hasan Shuvo, Imran Ashraf, Abdullah Al Mamun, Jia Uddin, Md Abdus Samad

“…Among them, XGBR outperformed, considering the accuracy, mean square error, and R2 score. We also used Explainable Artificial Intelligence (XAI), Local Interpretable Model (LIME), and SHapley Additive exPlanations (SHAP) to determine software fault features. …”
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Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete by Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, Naseer Muhammad Khan, Majid Khan, Fahid Aslam, Diaa Salama Abd Elminaam

“…The performance of the developed algorithms was assessed using several error metrices, k-fold validation, and residual assessment etc. …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…This study examines the impact of incorporating these constraints on prediction accuracy using four manually fine-tuned ML algorithms: Random Forest (RF), Support Vector Regression (SVR), XGBoost (XGB), and Artificial Neural Network (ANN). …”
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Crosswalk between HRSD and MADRS outcomes for rTMS in patients with depression by Xiao Chen, Fidel Vila-Rodriguez, Zafiris J Daskalakis, Daniel M Blumberger, Jonathan Downar, Chao-Gan Yan, Tyler S Kaster

“…We used five crosswalk models: (1) a pharmacotherapy equipercentile model, (2) an rTMS equipercentile model, (3) a linear regression model, (4) a random forest (RF) regression model and (5) a support vector regression (SVR) model. …”
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An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty by Mohsen Pourmohammad Shahvar, Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina, Giovanni Marsella

“…Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). …”
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