Game Theory-Based Comparison of Disaster Risk Assessment for Two Landfall Typhoons: A Case Study of Jilin Province's Impact

被引:0
作者
Dong, Zhennan [1 ]
Zhu, Dan [1 ]
Zhang, Yichen [1 ]
Zhang, Jiquan [2 ]
Yang, Xiufeng [3 ]
Huang, Fanfan [1 ]
机构
[1] Changchun Inst Technol, Coll Jilin Emergency Management, Changchun 130012, Peoples R China
[2] Northeast Normal Univ, Inst Nat Disaster Res, Sch Environm, Changchun 130024, Peoples R China
[3] Changchun Meteorol Bur, Changchun 130012, Peoples R China
关键词
northward typhoon; disaster risk; game theory; entropy weight; SOUTH CHINA SEA;
D O I
10.3390/atmos15121434
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process-Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and comparison of the disaster risk caused by two typhoons, Maysak and Haishen, in Jilin Province. Game theory enhances precision in evaluation beyond conventional approaches, effectively addressing the shortcomings of both subjective and objective weighting methods. Typhoon Maysak and Typhoon Haishen exhibit analogous tracks. They have successively exerted an impact on Jilin Province, and the phenomenon of overlapping rain areas is a crucial factor in triggering disasters. Typhoon Maysak features stronger wind force and greater hourly rainfall intensity, while Typhoon Haishen has a longer duration of rainfall. Additionally, Typhoon Maysak causes more severe disasters in Jilin Province. With regard to the four dimensions of disaster risk, the analysis of hazards reveals that the areas categorized as high risk and above in relation to the two typhoons are mainly located in the central-southern and eastern regions of Jilin Province. Typhoon Maysak has a slightly higher hazard level. During the exposure assessment, it was determined that the high-risk areas occupied 16% of the gross area of Jilin Province. It is mainly concentrated in three economically developed cities, as well as some large agricultural counties. In the context of vulnerability analysis, regions classified as high risk and above constitute 54% of the overall area. The areas classified as having high vulnerability are predominantly located in Yushu, Nong'an, and Songyuan. From the analysis of emergency response and recovery ability, Changchun has strong typhoon disaster prevention and reduction ability. This is proportional to the local level of economic development. The mountainous areas in the east and the regions to the west are comparatively weak. Finally, the comprehensive typhoon disaster risk zoning indicates that the zoning of the two typhoons is relatively comparable. When it comes to high-risk and above areas, Typhoon Maysak accounts for 38% of the total area, while Typhoon Haishen occupies 47%. The regions with low risk are predominantly found in Changchun, across the majority of Baicheng, and at the intersection of Baishan and Jilin. Upon comparing the disasters induced by two typhoons in Jilin Province, it was observed that the disasters caused by Typhoon Maysak were considerably more severe than those caused by Typhoon Haishen. This finding aligns with the intense wind and heavy rainfall brought by Typhoon Maysak.
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页数:21
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  • [1] Ensemble generation for hurricane hazard assessment along the United States' Atlantic coast
    Ansari, A. Hojjat
    Olyaei, M. A.
    Heydari, Z.
    [J]. COASTAL ENGINEERING, 2021, 169
  • [2] Comprehensive evaluation and optimal management of extreme disaster risk in Chinese urban agglomerations by integrating resilience risk elements and set pair analysis
    Chen, Liang
    Chang, Ming
    Yang, Haonan
    Xiao, Yi
    Huang, Huan
    Wang, Xinyuan
    [J]. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2024, 111
  • [3] A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China
    Chen, Peiyan
    Yu, Hui
    Xu, Ming
    Lei, Xiaotu
    Zeng, Feng
    [J]. FRONTIERS OF EARTH SCIENCE, 2019, 13 (04) : 672 - 681
  • [4] Regional Rainfall Damage Functions to Estimate Direct Economic Losses in Rainstorms: A Case Study of the 2016 Extreme Rainfall Event in Hebei Province of China
    Chen, Xiaojuan
    Xu, Yifu
    Li, Ting
    Wei, Jun
    Wu, Jidong
    [J]. INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE, 2024, 15 (04) : 508 - 520
  • [5] Assessment of Typhoon Precipitation Forecasts Based on Topographic Factors
    Chen, Xu-Zhe
    Ma, Yu-Long
    Lin, Chun-Qiao
    Fan, Ling-Li
    [J]. ATMOSPHERE, 2023, 14 (11)
  • [6] A Predictive Model for Estimating Damage from Wind Waves during Coastal Storms
    Choo, Yeon Moon
    Chun, Kun Hak
    Jeon, Hae Seong
    Sim, Sang Bo
    [J]. WATER, 2021, 13 (09)
  • [7] Assessment of vegetation damage by three typhoons (Bavi, Maysak, and Haishen) in Northeast China in 2020
    Dong, Guannan
    Liu, Zhengjia
    Du, Guoming
    Dong, Jinwei
    Liu, Kai
    [J]. NATURAL HAZARDS, 2022, 114 (03) : 2883 - 2899
  • [8] Stochastic Simulation of Typhoon in Northwest Pacific Basin Based on Machine Learning
    Fang, Yong
    Sun, Yanhua
    Zhang, Lu
    Chen, Gengxin
    Du, Mei
    Guo, Yunxia
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [9] Storm surge variation along the coast of the Bohai Sea
    Feng, Jianlong
    Li, Delei
    Li, Yan
    Liu, Qiulin
    Wang, Aimei
    [J]. SCIENTIFIC REPORTS, 2018, 8
  • [10] Quantitative Assessment of Typhoon Disaster Risk at County Level
    Guo, Guizhen
    Yin, Jie
    Liu, Lulu
    Wu, Shaohong
    [J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (09)