Abstract
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.
Highlights
The predominant role of several nonstationary and nonlinear variables in transformation of rainfall into runoff makes it difficult to comprehend [1]
In further analysis, each input was pre-processed using the maximal overlap discrete wavelet transformation (MODWT) technique to assess the effect of wavelet transformation on Single Decision Tree (SDT) models
From the comparative analysis of the simple and MODWT-based models, it can be concluded that MODWT-based models outperformed simple models, except SDT, where the simple SDT model yielded higher accuracy than MODWT-SDT models
Summary
The predominant role of several nonstationary and nonlinear variables in transformation of rainfall into runoff makes it difficult to comprehend [1]. The direct involvement of rainfall in runoff generation and runoff in streams, rivers, and even floods, makes it one of the most focused hydrological phenomena. The natural disasters such as fluvial and pluvial floods and hydrological droughts are determined by the rainfall–runoff relationship of any basin [3]. From the equation it can be observed that both rainfall/precipitation (P) and runoff (R) play a decisive role in storage change (dS), while infiltration (I) and evaporation losses (E) are to be considered for an accurate estimation of change in water storage
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