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Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m3/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy.
Khan et al. (2021) studied this question.