Author

Mohammad Zounemat‐Kermani

Shahid Bahonar University of Kerman

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Open access 2026

River Water Temperature Prediction Based on Boosting Algorithms and Ensemble Empirical Mode Decomposition: CatBoost, XGBoost, LightGBM versus AdaBoost

This study presents a hybrid modelling framework for predicting river water temperature (Tw) by combining advanced boosting algorithms with signal decomposition techniques. Four models were compared: (i) categorical boosting (CatBoost), (ii) extreme gradient boosting (XGBoost), (iii) adaptive boosting (AdaBoost), and (...

K. Merabet, F. Nunno, F. Granata et al. · Revista Internacional de Mét... · 0 citations

Open access Sep 2026

River water temperature prediction using REMD, RLMD, and SVMD coupled with deep learning models

Four deep learning models were used for modelling daily water temperature time series at four rivers stations in Poland, and the smallest RMSE and MAE are obtained for the models using the signal decomposition, whereas the largest errors metrics are achieved by the single DL models.

N. Daif, Dong Sop Rhee, O. Kisi et al. · Scientific Reports · 0 citations

Open access Aug 2026

Physics-informed hybrid GR4J–XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis

This study proposes a physics-informed hybrid modeling framework that integrates the GR4J conceptual model with the XGBoost machine learning algorithm, enabling a process-based representation of catchment dynamics within the machine learning framework.

Ozgur Kisi, S. Heddam, K. Parmar et al. · Scientific Reports · 0 citations