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Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems

Affiliations
  • 1Civil Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
  • 2Mathematics and Engineering Physics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
  • 3Civil Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt.
  • 4Civil Engineering Department, College of Engineering, Shaqra University, Dawadmi, 11911, Saudi Arabia.
  • 5Chair of Natural Hazards and Mineral Resources, Geology and Geophysics Department, King Saud University, Riyadh, 11451, Saudi Arabia.
  • 6Deanship of Scientific Research, King Saud University, Riyadh, 145111, Saudi Arabia.
  • 7Civil Engineering Department, Faculty of Engineering, South Valley University, Qena, 83521, Egypt.

Published on:

July 29, 2024

Abstract

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Keywords:

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