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Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups

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  • Xue Jin

    (School of Economics, Ocean University of China, Qingdao 266100, China
    College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China)

  • Xiaoxia Shi

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Jintian Gao

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Tongbin Xu

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Kedong Yin

    (School of Economics, Ocean University of China, Qingdao 266100, China
    College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
    Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China)

Abstract

Storm surge has become an important factor restricting the economic and social development of China’s coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation.

Suggested Citation

  • Xue Jin & Xiaoxia Shi & Jintian Gao & Tongbin Xu & Kedong Yin, 2018. "Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups," IJERPH, MDPI, vol. 15(4), pages 1-19, March.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:604-:d:138185
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    References listed on IDEAS

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    1. Xianwu Shi & Shan Liu & Saini Yang & Qinzheng Liu & Jun Tan & Zhixing Guo, 2015. "Spatial–temporal distribution of storm surge damage in the coastal areas of 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. 79(1), pages 237-247, October.
    2. Stéphane Hallegatte & Nicola Ranger & Olivier Mestre & Patrice Dumas & Jan Corfee-Morlot & Celine Herweijer & Robert Wood, 2011. "Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen," Climatic Change, Springer, vol. 104(1), pages 113-137, January.
    3. S. Dube & Indu Jain & A. Rao & T. Murty, 2009. "Storm surge modelling for the Bay of Bengal and Arabian Sea," 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. 51(1), pages 3-27, October.
    4. Adam Rose & Shu‐Yi Liao, 2005. "Modeling Regional Economic Resilience to Disasters: A Computable General Equilibrium Analysis of Water Service Disruptions," Journal of Regional Science, Wiley Blackwell, vol. 45(1), pages 75-112, February.
    5. P. K. Narayan, 2003. "Macroeconomic impact of natural disasters on a small island economy: evidence from a CGE model," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 721-723.
    6. David Byrne & Kevin Horsburgh & Brian Zachry & Paolo Cipollini, 2017. "Using remotely sensed data to modify wind forcing in operational storm surge forecasting," 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. 89(1), pages 275-293, October.
    7. Kedong Yin & Ya Zhang & Xuemei Li, 2017. "Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data," IJERPH, MDPI, vol. 14(11), pages 1-18, November.
    8. Yasuhide Okuyama, 2007. "Economic Modeling for Disaster Impact Analysis: Past, Present, and Future," Economic Systems Research, Taylor & Francis Journals, vol. 19(2), pages 115-124.
    9. Stéphane Hallegatte, 2008. "An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina," Post-Print hal-00716550, HAL.
    10. Mampei Hayashi, 2012. "A Quick Method for Assessing Economic Damage Caused by Natural Disasters: An Epidemiological Approach," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 18(4), pages 417-427, November.
    11. Shuo Yang & Xin Liu & Qiang Liu, 2016. "A storm surge projection and disaster risk assessment model for China coastal areas," 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. 84(1), pages 649-667, October.
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    Cited by:

    1. Ning Zhang & Zaiwu Gong & Kedong Yin & Yuhong Wang, 2018. "Special Issue “Decision Models in Green Growth and Sustainable Development”," IJERPH, MDPI, vol. 15(6), pages 1-8, May.
    2. Xiaotong Sui & Mingzhao Hu & Haoyun Wang & Lingdi Zhao, 2023. "Improved elasticity estimation model for typhoon storm surge losses 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. 116(2), pages 2363-2381, March.

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