Predicting hydrological dynamics in semi-arid regions is challenging due to climate instability. This study proposes a hybrid modeling framework integrating Seasonal-Trend decomposition (STL), SARIMA, and Long Short-Term Memory (LSTM) networks to capture seasonal and nonlinear residual patterns. A modular Intelligent Information System (IIS) architecture is designed to operationalize this approach using real-time Central Asian data. Results demonstrate the hybrid model significantly outperforms standalone methods, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.85–0.96 and reducing RMSE by 18–35%. This framework provides a robust operational tool for data-driven water resource management.
| Mualliflar | Nasridinov, Rustamjon |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-05-17 |
| Son | 2 |
| Betlar | 182-194 |
| Til | Rus |
hybrid model, deep learning, intelligent algorithms, methods of data collection, evaluation of predictive ability of the model, study of correlations between parameters., forecasting;
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