Wavelet Enhanced LSTM for Accurate Streamflow Modeling Addressing Nonlinearity and Nonstationarity in Hydrological Data

Flood prediction is challenging due to increasing climate variability and nonlinear, nonstationary hydrological data. We propose a novel hybrid model, Wavelet Long Short-Term Memory (WLSTM), combining wavelet transforms with LSTM networks. Wavelet decomposition allows separation of frequency components, enhancing LSTM’s ability to learn both short- and long-term dependencies. Applied to streamflow data from 16 stations in Hessen (2000–2017), WLSTM reduced RMSE by 66.43% and MAPE by 45.49%, while increasing R² by 2.06%. These improvements highlight its effectiveness in modeling complex hydrological dynamics. WLSTM offers a robust tool for adaptive flood risk management under climate change.