摘要
基于投影寻踪聚类模型(PPC),结合基于实数编码的加速遗传算法(RAGA),对上海市进行了自然灾害社会脆弱性评估的尝试。结果表明:①灾害社会脆弱性最高的为崇明县,其次为宝山区和金山区;②灾害社会脆弱性最低的是黄埔区,其次是徐汇区和静安区;③总体而言,灾害脆弱性较低的地区集中于上海城市中心区,而城市边缘区的社会脆弱性一般较高。
A approach combining with the Projection Pursuit Classification Model （PPC） and Real Coding based Acceleration Genetic Algorithm （RAGA） is used to evaluate social vulnerability. A trial application of this method is made to Shanghai, China. The results show that （ 1 ） the social vulnerability of Chongming county, Baoshan district and Jinshan district is higher than that in other regions;（2） the social vulnerability of Huangpu district is the lowest, and then is Xuhui distric and Jingan district; （3） in Shanghai, the social vulnerability of fringe area is higher than that of the center.
出处
《灾害学》
CSCD
北大核心
2012年第1期98-100,110,共4页
Journal of Catastrophology
基金
国家自然科学基金项目(40701005)
国家科技支撑计划课题(2008BAK50B07)
国家减灾中心项目
关键词
自然灾害
社会脆弱性
投影寻踪聚类模型
实数编码加速遗传算法(RAGA)
上海市
natural hazards
social vulnerability
projection pursuit classification model （PPC）
Real Coding based Acceleration Genetic Algorithm （RAGA）
Shanghai
