Author

M. Ptak

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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