Modeling and mapping spatial distribution of baseline soil organic carbon stock, a case of West Hararghe, Oromia Regional State, Eastern Ethiopia by Martha Kidemu Negassa, Mitiku Haile, Gudina Legesse Feyisa, Lemma Wogi, Feyera Merga

“…Eighteen environmental covariates were acquired from satellite sources, digital elevation model (DEM), and maps. A random forest model was fitted to the data. The accuracy of the prediction was tested using the 10-fold cross-validation method. …”
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The Impact of Snow Cover on River Discharge Simulation: Insights from the Barandozchay River Basin by Haleh Hashemi, Hossein Rezaie, Keivan Khalili, Amin Amini

“…The results indicate that the Random Forest model outperforms the others in accuracy and generalization, while SVM demonstrates improved predictive capabilities with the inclusion of snow cover data. …”
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Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity by Nasir Khan, Mehdi Razavifar, Qazi Adnan Ahmad, Muhammad Siyar, Masoud Riazi, Waqar Khan, Jafar Qajar

“…In particular, a Random Forest Regressor (RFR) model was applied, with results demonstrating high reliability based on RMSE and R2 values for training (3.3395, 0.9764), validation (3.0166, 0.9602), and testing (2.4778, 0.9557). …”
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Nonlinear characteristics and prediction of gas and temperature in coal spontaneous combustion oxidation process by Ping ZHAN, Changkui LEI, Renhui CHENG

“…The characteristic indicators during coal oxidation were analyzed, including high-temperature point migration, gas volume fraction changes, oxygen consumption rate, and gas production rate. A random forest (RF) model for nonlinear prediction of coal temperature was established and validated using on-site monitoring data. …”
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Estimating water levels in reservoirs using Sentinel-2 derived time series of surface water areas: A case study of 20 reservoirs in Burkina Faso by Audrey Kantz Dossou Codjia, Komlavi Akpoti, Moctar Dembélé, Roland Yonaba, Tazen Fowe, Soumahila Sankande, Modeste G. Déo-Gratias Koissi, Sander J. Zwart

“…In this study, the surface area of 20 reservoirs is first determined using a Random Forest classifier and Sentinel-2 images acquired between 2015 and 2022. …”
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Machine learning-based prediction method for open-pit mining truck speed distribution in manned operation by Changyou XU, Gang CHEN, Qiuxia ZHANG, Bo WANG, Hongwang ZHANG, Hongrui LI, Weiwei QIN, Muyang LI

“…Among these models, the Random Forest-based model exhibited lower mean squared error and a higher coefficient of determination, outperforming the XGBoost-based model. …”
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Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Carlos Pedro Boechat Soares, José Marinaldo Gleriani, Daniel Henrique Brada Binoti, Carlos Alberto Araújo Júnior, Mohd Ibrahim Shapiai, Leonardo Ippolito Rodrigues, Tassius Menezes Araújo, Helio Garcia Leite

“…Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.…”
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An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…Performance metrics such as classification error rate and precision are used for evaluation purposes. …”
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The reduction of the standard of census route length using double lamination of sample by V. M. Glushkov

“…Census data were recorded and processed by two methods: traditional - winter route census (WRC) with grouping of sample by category of land (forest, field, swamp), and the new one with grouping of segments of the route according to the level of linear density (trace / 1 km of route), separately for each stratum. …”
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Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses by Xiufeng Chen, Yanbin Yuan, Tao Xiong, Sicong He, Heng Dong

“…Therefore, in this study, two phenological metrics for the Start of Growing Season (SOS) and the End of Growing Season (EOS) were extracted from the phenology of deciduous forests in the middle and high latitudes of the Northern Hemisphere, utilizing SIF products at scales of 1 km, 5 km, and 50 km, and applying the Savitzky-Golay filtering method along with the dynamic threshold method. …”
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BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos, Antonios Papamanolis

“…They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). …”
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Studies comparing the effectiveness of models for drying bitter gourd slices by Dinh Anh Tuan Tran, Tuan Nguyen Van, Thi Khanh Phuong Ho

“…Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE) and mean absolute percentage error (MAPE). …”
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Towards enhancing field‐based vegetation monitoring: A deep learning approach for species coverage estimation from ground‐level imagery by Pauline Müller, Stefano Puliti, Johannes Breidenbach

“…We developed a deep learning pipeline relying on YOLOv8 models to segment species and estimate the percentage cover (%) of Vaccinium myrtillus (blueberry) and Vaccinium vitis‐idaea (lingonberry), two key understory species in boreal forests. We used 138 nadir and downward‐looking images of the forest floor captured in correspondence with 50 × 50 cm vegetation sub‐plots assessed within National Forest Inventory (NFI) plots. …”
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Empirical Study on Myopia Identification Using CNN Hereditary Model for Resource Constrained Ophthalmology by Aqila Nazifa, Manisha Shivaram Joshi, Soumya Ramani

“…During capturing, the photos are processed using recent image processing techniques to identify any irregularities or asymmetries that may indicate refractive errors. By comparing our method to other current models, we hope to illustrate the advantage of our Hereditary model, which combines a random forest and a convolutional neural network, in accurately diagnosing and classifying refractive errors. …”
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Leveraging Artificial Intelligence in Public Health: A Comparative Evaluation of Machine-Learning Algorithms in Predicting COVID-19 Mortality by Eric B. Weiser

“…The four ML models were trained and tested on this dataset, with performance assessed using R2, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). …”
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Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…Five different machine learning regression models, namely linear regression (LR), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were employed and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) along with R2 for predicting the daily ground-level PM10 concentration using AOD, land cover data, and meteorological parameters. …”
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Apply Ridge Regression Model to Predict the Lateral Velocity Difference of Tight Reservoirs by HAN Longfei, ZHANG Yongfei, WANG Miaomiao, LI Yu

“…This error cannot meet the subsequent construction requirements. …”
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Machine learning algorithms to predict the tensile strength of novel composite materials by S. Sathees Kumar, P. Shyamala, Pravat Ranjan Pati

“…Five regression algorithms such as polynomial regression, bagging regression, random forest, XGBoost, and gradient boosting were trained and evaluated using five-fold cross-validation and standard error metrics. …”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…R²), mean absolute error (MAE), and root mean square error (RMSE), were KNN < DT < RF < XGBoost. …”
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The ecological forecast limit revisited: Potential, absolute and relative system predictability by Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington, William J. Castillo, Florian Pappenberger, Carsten F. Dormann

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