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Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China

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  • Zhongping Zeng

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
    Non-Traditional Security Center, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yujia Li

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jinyu Lan

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Abdur Rahim Hamidi

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
    Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed.

Suggested Citation

  • Zhongping Zeng & Yujia Li & Jinyu Lan & Abdur Rahim Hamidi, 2021. "Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6929-:d:578219
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    References listed on IDEAS

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    1. Mahya Norallahi & Hesam Seyed Kaboli, 2021. "Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 119-137, March.
    2. Desirée Tullos & Elizabeth Byron & Gerald Galloway & Jayantha Obeysekera & Om Prakash & Yung-Hsin Sun, 2016. "Review of challenges of and practices for sustainable management of mountain flood hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1763-1797, September.
    3. J. F. Rosser & D. G. Leibovici & M. J. Jackson, 2017. "Rapid flood inundation mapping using social media, remote sensing and topographic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 103-120, May.
    4. Kaizhong Li & Shaohong Wu & Erfu Dai & Zhongchun Xu, 2012. "Flood loss analysis and quantitative risk assessment in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 737-760, September.
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    Cited by:

    1. Huan Xu & Ying Wang & Xiaoran Fu & Dong Wang & Qinghua Luan, 2023. "Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China," IJERPH, MDPI, vol. 20(5), pages 1-23, March.

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