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

Zafar Iqbal, Shamsuddin Shahid, Tarmizi Ismail, Zulfaqar Sa’adi, Aitazaz Farooque and Zaher Mundher Yaseen
Additional contact information
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
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

Sustainability, 2022, vol. 14, issue 11, 1-30

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.

Keywords: satellite rainfall; distributed hydrological model; flood forecast; machine learning; rainfall extremes (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/11/6620/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/11/6620/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:11:p:6620-:d:826747

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().