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Article

Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan

1
Department of Agricultural Engineering, Faculty of Agricultural Sciences & Technology, Bahauddin Zakariya University, Multan 60000, Pakistan
2
NUST Institute of Civil Engineering, School of Civil & Environmental Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Kwok-wing Chau
Water 2021, 13(24), 3528; https://doi.org/10.3390/w13243528
Received: 20 October 2021 / Revised: 3 December 2021 / Accepted: 3 December 2021 / Published: 9 December 2021
(This article belongs to the Section Hydrology)
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. View Full-Text
Keywords: machine learning; ANN; single tree boost; decision tree; decision tree forest; Pothohar region machine learning; ANN; single tree boost; decision tree; decision tree forest; Pothohar region
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MDPI and ACS Style

Khan, M.T.; Shoaib, M.; Hammad, M.; Salahudin, H.; Ahmad, F.; Ahmad, S. Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan. Water 2021, 13, 3528. https://doi.org/10.3390/w13243528

AMA Style

Khan MT, Shoaib M, Hammad M, Salahudin H, Ahmad F, Ahmad S. Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan. Water. 2021; 13(24):3528. https://doi.org/10.3390/w13243528

Chicago/Turabian Style

Khan, Muhammad T., Muhammad Shoaib, Muhammad Hammad, Hamza Salahudin, Fiaz Ahmad, and Shakil Ahmad. 2021. "Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan" Water 13, no. 24: 3528. https://doi.org/10.3390/w13243528

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