Modeling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application To the zhaiyk-caspian basin, Kazakhstan

Alimkulov S. Makhmudova L. Satenova B. Tursunova A. Birimbayeva L. Talipova E. Abdibekov D. Smagulov Z. Alzhanov O.
2025 Springer Science and Business Media Deutschland GmbH

Earth Systems and Environment
2025

The study presents a comparative assessment of eight machine learning (ML) algorithms - Random Forest (RF), Lasso Regression (LASSO), AdaBoost (ADB), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN) - for modeling daily river discharge at ten hydrological stations within the Zhaiyk - Caspian water management basin. Model performance was evaluated using mean absolute error (MAE), mean squared error (MSE), and symmetric mean absolute percentage error (SMAPE). The highest predictive accuracy (MAE ≈ 0.3) was achieved by ensemble tree-based methods (Random Forest, CatBoost, Gradient Boosting, LightGBM, XGBoost), while LASSO and AdaBoost exhibited the weakest performance (MAE ≈ 22). Identifying the most significant predictors enhanced both model interpretability and forecasting quality. The findings highlight the importance of tailoring ML approaches to the specific characteristics of river basins and suggest promising prospects for their integration with physically based hydrological models to improve river discharge forecasting and strengthen water resources management under climate change conditions.

AdaBoost , Algorithm , CatBoost , Climate , Gradient boosting , Hydrological regime , KNN , LASSO , LightGBM , Machine learning , Modelling , Random forest , Water resources , XGBoost

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JSC Institute of Geography and Water Security, Almaty, Kazakhstan
al-Farabi Kazakh National University, Almaty, Kazakhstan

JSC Institute of Geography and Water Security
al-Farabi Kazakh National University

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