Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) DOI Open Access
Igor Leščešen, Mitra Tanhapour, Pavla Pekárová

и другие.

Water, Год журнала: 2025, Номер 17(6), С. 907 - 907

Опубликована: Март 20, 2025

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects climate change, in particular the shift precipitation patterns increasing frequency extreme weather events, necessitate development advanced models. This study investigates application long short-term memory (LSTM) neural networks predicting runoff Velika Morava catchment Serbia, representing a pioneering LSTM this region. uses daily runoff, temperature data from 1961 to 2020, interpolated using inverse distance weighting method. model, which was optimized trial-and-error approach, showed high prediction accuracy. For station, model mean square error (MSE) 2936.55 an R2 0.85 test phase. findings highlight effectiveness capturing nonlinear hydrological dynamics, temporal dependencies regional variations. underlines potential models improve management strategies Western Balkans.

Язык: Английский

Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) DOI Open Access
Igor Leščešen, Mitra Tanhapour, Pavla Pekárová

и другие.

Water, Год журнала: 2025, Номер 17(6), С. 907 - 907

Опубликована: Март 20, 2025

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects climate change, in particular the shift precipitation patterns increasing frequency extreme weather events, necessitate development advanced models. This study investigates application long short-term memory (LSTM) neural networks predicting runoff Velika Morava catchment Serbia, representing a pioneering LSTM this region. uses daily runoff, temperature data from 1961 to 2020, interpolated using inverse distance weighting method. model, which was optimized trial-and-error approach, showed high prediction accuracy. For station, model mean square error (MSE) 2936.55 an R2 0.85 test phase. findings highlight effectiveness capturing nonlinear hydrological dynamics, temporal dependencies regional variations. underlines potential models improve management strategies Western Balkans.

Язык: Английский

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