S. Heddam

Rainfall–runoff prediction in an ungauged catchment using an integrated HEC-HMS and machine learning framework

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 (...

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.

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.