Runoff coefficient modelling using Long Short-Term Memory (LSTM) in the Rur catchment, Germany

This research examines the effectiveness of Long Short-Term Memory (LSTM) models in predicting runoff coefficient (Rc) within the Rur basin at the Stah outlet (Germany) during the period from 1961 to 2021; monthly data of temperature (T), precipitation (P), soil water storage (SWS), and total evaporation (ETA) are used as an input. Because of the complexity in predicting undecomposed Rc time series due to noise, a novel approach incorporating discrete wavelet transform (DWT) to decompose the original Rc at five levels is proposed. The investigation identifies overfitting challenges at level-1, gradually mitigated in subsequent decomposition levels, particularly in level-2, while other levels remain tuned. Reconstructing Rc using modelled decomposition coefficients yielded Nash-Sutcliffe efficacy (NSE) values of 0.88, 0.79, and 0.74 for the training, validation, and test sets, respectively. Comparative analysis highlights that modelling undecomposed Rc with LSTM yields to a minor accuracy, emphasizing the pivotal role of decomposition techniques in tandem with LSTM for enhanced model performances. This study provides novel insights to address challenges related to noise effects and temporal dependencies in Rc modelling; through a comprehensive analysis of the interplay between atmospheric conditions and observed data, the research contributes in advancing predictive modelling in hydrology.