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Hazard assessment and regionalization of highway flood disasters in China

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  • Chao Yin

    (Shandong University of Technology)

Abstract

China has suffered from serious highway flood disasters (HFDs) due to the deterioration and damages of natural environment, and a comprehensive understanding of HFDs from a spatial aspect has become the most important preconditions to improve the highway anti-disaster capacities. Embedded wavelet neural network, particle swarm optimization–improved support vector machine and logistic regression method were applied to the hazard assessment of HFDs in plain areas. Combined with the achievements in mountainous areas, the hazard regionalization of HFDs in China was proposed. The results show that the extreme hazard areas, severe hazard areas, moderate hazard areas and micro-hazard areas account for 22.6%, 24.4%, 20.7% and 32.3% of China’s total land areas, respectively. The investigated highway segments suffering from flood disasters located in extreme hazard areas and severe hazard areas account for 48.25% and 25.87%, respectively, indicating the hazard regionalization is consistent with the actual disaster distribution situations. The research results can be regarded as the theoretical foundations for macro-policy formulation for prevention and control of HFDs.

Suggested Citation

  • Chao Yin, 2020. "Hazard assessment and regionalization of highway flood disasters 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. 100(2), pages 535-550, January.
    • Handle: RePEc:spr:nathaz:v:100:y:2020:i:2:d:10.1007_s11069-019-03824-9
    DOI: 10.1007/s11069-019-03824-9
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    References listed on IDEAS

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    1. Anvarifar, Fatemeh & Voorendt, Mark Z. & Zevenbergen, Chris & Thissen, Wil, 2017. "An application of the Functional Resonance Analysis Method (FRAM) to risk analysis of multifunctional flood defences in the Netherlands," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 130-141.
    2. Chao Yin & Jinglei Zhang, 2018. "Hazard regionalization of debris-flow disasters along highways 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. 91(1), pages 129-147, April.
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    Citations

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    Cited by:

    1. Chao Yin & Haoran Li & Fa Che & Ying Li & Zhinan Hu & Dong Liu, 2020. "Susceptibility mapping and zoning of highway landslide disasters in China," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    2. Milad Zamanifar & Timo Hartmann, 2020. "Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks," 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. 104(1), pages 1-25, October.
    3. Chao Yin & Zhanghua Wang & Xingkui Zhao, 2022. "Spatial prediction of highway slope disasters based on convolution neural networks," 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. 113(2), pages 813-831, September.
    4. T. E. Ologunorisa & O. Obioma & A. O. Eludoyin, 2022. "Urban flood event and associated damage in the Benue valley, Nigeria," 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. 111(1), pages 261-282, March.

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