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Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models

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Abstract

Floods are among the most destructive disasters because they cause immense damage to human life, property (land and buildings), and resources. They also slow down a country’s economy. Due to the dynamic and complex nature of floods, it is difficult to predict the areas that are prone to flooding. In this study, an attempt was made to create a suitability map for future urban development based on flood vulnerability maps for the catchment area of Taif, Saudi Arabia. Three models were used for this purpose, including bivariate (FR), multivariate (LR), and machine learning (SVM) were used. Thirteen parameters were used as flood-contributing parameters. The inventory map was constructed using field surveys, historical data, analysis of RADAR (Sentinel-1A), and Google Earth imagery collected between 2013 and 2020. In general, 70% flood locations were randomly selected from the flood inventory map to generate the flood susceptibility model, and the remaining 30% of the flood locations were used for model validation. The flood susceptibility map was classified into five zones: very low, low, moderate, high, and very high. The AUC value used to predict the performance of the models showed that the accuracy reached 89.5, 92.0, and 96.2% for the models FR, LR, and SVM, respectively. Accordingly, the flood susceptibility map produced by the SVM model is accurate and was used to produce a flood vulnerability map with the help of urban and road density maps. Then slope and elevation maps were integrated with the flood vulnerability model to produce the final suitability map, which was classified into three zones: isolated zone, low suitability, and high suitability areas. The results showed that the highly suitable areas are located in the east and northeast of the Taif Basin, where the flood risk is low and very low. The results of this work will improve the land use planning of engineers and authorities and take possible measures to reduce the flood hazards in the area.

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Acknowledgements

The authors would like to thank the INSF.

Funding

This work was supported by the Iran National Science Foundation (INSF) under Grant No. 99011055.

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Authors and Affiliations

  1. Geology Department, Faculty of Science, Sohag University, Sohag, 82524, Egypt

    Ahmed M. Youssef

  2. Geological Hazards Center, Saudi Geological Survey, P.O. Box 54141, Jeddah, 21514, Kingdom of Saudi Arabia

    Ahmed M. Youssef

  3. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  4. Geology Department, Faculty of Science, South Valley University, Qena, 83523, Egypt

    Ali M. Mahdi

  5. Remote Sensing Department, Saudi Geological Survey (SGS), P.O. Box 54141, Jeddah, 21514, Saudi Arabia

    Saleh S. Matar

Authors
  1. Ahmed M. Youssef
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  2. Hamid Reza Pourghasemi
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  3. Ali M. Mahdi
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  4. Saleh S. Matar
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Contributions

AMY and HRP: Conceptualization, investigation, data curation, methodology, software, run models, analyzed results, writing—original draft, writing—review & editing. AMM: Methodology, designed experiments, writing—original draft, writing—review & editing. SSM: Investigation, methodology, analyzed results, writing—original draft & editing.

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Correspondence to Hamid Reza Pourghasemi.

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Youssef, A.M., Pourghasemi, H.R., Mahdi, A.M. et al. Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models. Environ Sci Pollut Res 30, 16081–16105 (2023). https://doi.org/10.1007/s11356-022-23140-3

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  • DOI: https://doi.org/10.1007/s11356-022-23140-3

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