An evolutionary deep learning model integrated with signal reconstruction for long-term hourly forecast of flood in the upper basin of the Gangjiang River
Deep learning has advanced flood forecasting through its strong nonlinear modeling capabilities. However, accuracy declines and robustness remain insufficient at longer forecast lead times. Therefore, an evolutionary deep learning model for flood forecasting was developed by integrating time-varying filter-based empirical mode decomposition (TVFEMD), sample entropy-based signal reconstruction (SE)
