Аңдатпа
Accurately forecasting reservoir water volumes is crucial for climate change adaptation, drought mitigation, and provides a basis for energy planning and management, particularly in continental basins, where hydrological variability poses challenges to traditional modeling. In turn, reduced reservoir inflows limit the water available to hydroelectric power plants for optimal hydroelectric generation. To address this issue, we propose a novel delta-based learning framework using extreme gradient boosting (XGBoost) that forecasts daily volume increments (ΔV) rather than total water volume. The proposed model represents a novel solution where standard machine learning approaches often struggle to capture short-term dynamics due to non-stationary trends in absolute water volume data. The model integrates meteorological and hydrological characteristics of the ERA5 reanalysis lag, is trained on 2019–2022 data, tested on 2023–2025 data, and is adapted to regional specificities. The results demonstrate exceptional performance: the reconstructed volume achieved an R² of 0.983, a Nash–Sutcliffe efficiency (NSE) of 0.983, and a Kling–Gupta efficiency (KGE) of 0.984. Most importantly, the delta-model-based approach reduced the root mean square error (RMSE) by 16 % compared to the baseline direct volume forecasting method, effectively eliminating systematic trend leakage. Performance analysis confirmed the physical dominance of delays in water volume and precipitation integrals. This study confirms that differential dynamics modeling significantly improves the stability and interpretability of forecasts, offering a reproducible and reliable tool. Keywords: reservoir forecasting, delta-based learning, hydrological machine learning, XGBoost, ERA5 reanalysis, multi-horizon prediction, remote sensing, predictive modeling.