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Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Author

Listed:
  • Zafar Iqbal

    (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Shamsuddin Shahid

    (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Tarmizi Ismail

    (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Zulfaqar Sa’adi

    (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    Centre for Environmental Sustainability and Water Security (IPASA), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Aitazaz Farooque

    (Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A4P3, Canada)

  • Zaher Mundher Yaseen

    (Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
    USQ’s Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
    New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq)

Abstract

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Suggested Citation

  • Zafar Iqbal & Shamsuddin Shahid & Tarmizi Ismail & Zulfaqar Sa’adi & Aitazaz Farooque & Zaher Mundher Yaseen, 2022. "Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods," Sustainability, MDPI, vol. 14(11), pages 1-30, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6620-:d:826747
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    References listed on IDEAS

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    4. Ling Zhang & Zhuotong Nan & Yi Xu & Shuo Li, 2016. "Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
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    1. Goksu Tuysuzoglu & Kokten Ulas Birant & Derya Birant, 2023. "Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars," Sustainability, MDPI, vol. 15(7), pages 1-24, March.

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