Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling

Abstract

Abstract Rainfall–runoff modelling is crucial for enhancing the effectiveness and sustainability of water resources. Conceptual models can have difficulties, such as coping with nonlinearity and needing more data, whereas data-driven models can be deprived of reflecting the physical process of the basin. In this regard, two hybrid model approaches, namely Génie Rural à 4 paramètres Journalier (GR4 J)–wavelet-based data-driven models (i.e., wavelet-based genetic algorithm–artificial neural network (WGANN); GR4 J–WGANN1 and GR4 J–WGANN2), were implemented to improve daily rainfall–runoff modelling. The novel GR4 J–WGANN1 hybrid model includes the outflow (QR) and direct flow (QD) obtained from the GR4 J model, and the GR4 J–WGANN2 hybrid model includes the soil moisture index (SMI) obtained from the GR4 J model as input data. In hybrid models, wavelet analysis and the Boruta algorithm were implemented to decompose input data and select wavelet components. Four gauging stations in the Eastern Black Sea and Kızılırmak basins in Turkey were used to observe modelling performance. The GR4 J model exhibited poor performance for extreme flow forecasting. The novel GR4 J–WGANN1 approach performed better than the GR4 J–WGANN2 model, and the hybrid models improved modelling performance up to 40% compared to the GR4 J model. In this regard, integrated conceptual–wavelet-based data-driven models can be useful for improving the conceptual model performance, especially regarding extreme flow forecasting.