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出 处:《统计与信息论坛》2016年第2期21-27,共7页Journal of Statistics and Information
基 金:国家自然科学基金项目<风暴潮灾害脆弱性测度及损失补偿对策研究>(71373247);国家自然科学基金项目<不对称PPP模式下风暴潮灾害保险合作机制研究>(71503238)
摘 要:以沿海11省市的风暴潮灾害风险为研究对象,采用遗传与粒子群混合算法对投影寻踪动态聚类(PPDC)模型进行优化,将粗糙集理论(RST)与修正的PPDC模型组合运用,对中国沿海地区风暴潮灾害的风险进行综合评估与区域等级划分。实证结果表明:广东和福建两省是中国风暴潮灾害的高风险区,风险评估值超过2.5,山东、浙江、海南和广西属于风暴潮灾害的中风险区,风险评估值处于[1.8,2.2]之间,江苏、天津、辽宁、河北和上海属于风暴潮灾害的低风险区,风险评估值低于1.5。研究结论为国家实施差异化的灾害风险管理战略提供了思路与参考。This paper combines Rough Set Theory(RST)with Projection Pursuit Dynamic Cluster(PPDC)model optimized by Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)to assess the comprehensive risk and make regional classification of storm surge disasters of eleven provinces and cities of Coastal Areas.The empirical results are as follows:Guangdong and Fujian are the high risk area of storm surge disaster in China,whose risk assessment scores are both more than 2.5.Shandong,Hainan,Guangxi and Zhejiang belong to the medium risk area of storm surge disaster and their risk assessment scores are between 1.8and 2.2.Tianjin,Liaoning,Hebei,Shanghai and Jiangsu belong to the low risk area of storm surge disaster whose risk assessment scores are all less than 1.5.The conclusion of this thesis is to provide a reference for the implementation of the risk management strategy of China.
分 类 号:X43[环境科学与工程—灾害防治] O213.9[理学—概率论与数理统计]
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