Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

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Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 8, 2025

Predicting water levels in glacier-fed lakes is vital for resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by Baishui River glacier on Yulong Snow Mountain. The introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), (GBM), decision tree (DT) through meta-learning to improve prediction accuracy under complex environmental conditions. High-frequency depth data, recorded every five minutes using an RBR logger, alongside variables such as temperature, wind speed, humidity, evaporation, solar radiation, rainfall, were analyzed. Temperature was identified most significant factor influencing levels, with importance score 15.69, followed atmospheric pressure (14.08) radiation (12.89), which impacted surface conditions evaporation. Relative humidity (10.24) speed (8.71) influenced lake stability mixing. QM outperformed individual models, achieved RMSE values 0.003 m (climate data) 0.001 (water data), R2 0.994 0.999, respectively. In comparison, XGB GBM exhibited higher lower scores. RF struggled 0.008 0.962, while DT performed better (RMSE: 0.006 but remained inferior proposed model. These findings demonstrate robustness approach handling particularly where fall short. highlights potential enhanced systems, recommending future research directions incorporate deep long-term forecasting expand capabilities global scale.

Language: Английский

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Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis

Chemosphere, Journal Year: 2025, Volume and Issue: 376, P. 144278 - 144278

Published: March 7, 2025

Language: Английский

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Key factors affecting groundwater nitrate levels in the Yinchuan Region, Northwest China: research using the eXtreme Gradient Boosting (XGBoost) model with the SHapley Additive exPlanations (SHAP) method

Environmental Pollution, Journal Year: 2024, Volume and Issue: 364, P. 125336 - 125336

Published: Nov. 19, 2024

Language: Английский

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