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Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran

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Abstract

This study aims at optimizing the support vector regression (SVR) model using four metaheuristic methods, Harris hawks optimization (HHO), particle swarm optimization (PSO), gray wolf optimizer (GWO), and bat algorithm (BA). The intent is to create a reliable flood susceptibility map (FSM). In this regard, a flood inventory map for 617 flood locations was generated from the Google earth engine (GEE). Four hundred and thirty-two random locations (70%) were used for spatial flood susceptibility modeling, and 185 random locations (30%) were selected for testing hybrid approaches. Based on the available data and literature, the following eleven factors were selected: altitude, slope angle, slope aspect, plan curvature, stream power index (SPI), topographic wetness index (TWI), distance to river, lithology, drainage density, land use, and rainfall. The normalized frequency ratio (NFR) method was used to obtain a weight for each class of each factor. Next, flood susceptibility maps were produced by SVR-HHO, SVR-PSO, SVR-GWO, and SVR-BA hybrid models. The prediction power of hybrid models was assessed using various indicators of sensitivity, specificity, accuracy, kappa coefficient, receiver operating curve (ROC) diagram, mean square error (MSE), and root-mean-square error (RMSE). Validation results indicated the area under the curve (AUC) of 85.8%, 85.7%, 85.5%, and 84.6% for the SVR-HHO, SVR-GWO, SVR-BA, and SVR-PSO hybrid models, respectively. The results from testing phase reveal the best performance of the SVR-HHO model (RMSE = 0.401, MSE = 0.160, sensitivity = 0.822, specificity = 0.800, accuracy = 0.811, and kappa = 0.622). The SVR-PSO model had a poor performance (RMSE = 0.406, MSE = 0.164, sensitivity = 0.827, specificity = 0.773, accuracy = 0.80, and kappa = 0.60). It can be concluded that the map produced by SVR-HHO is a feasible approach for modeling flood susceptibility.

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Acknowledgements

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and project of Data Construction for Artificial Intelligence Learning funded by National Information Society Agency and Ministry of Science and ICT.

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Authors and Affiliations

  1. Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sina Paryani & Mojgan Bordbar

  2. Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea

    Changhyun Jun

  3. Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 10691, Stockholm, Sweden

    Mahdi Panahi

  4. Division of Science Education, College of Education, Kangwon National University, # 4-301, Gangwondaehak-gil, Chuncheon- si, Gangwon-do, 24341, Republic of Korea

    Mahdi Panahi

  5. Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon, 34132, Republic of Korea

    Mahdi Panahi & Saro Lee

  6. Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA

    Sayed M. Bateni

  7. Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE, 68588, USA

    Christopher M. U. Neale

  8. Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Hamidreza Moeini

  9. Department of Geophysical Exploration, Korea University of Science and Technology,, 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea

    Saro Lee

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  1. Sina Paryani
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  2. Mojgan Bordbar
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  3. Changhyun Jun
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Paryani, S., Bordbar, M., Jun, C. et al. Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran. Nat Hazards 116, 837–868 (2023). https://doi.org/10.1007/s11069-022-05701-4

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