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基于GIS和Logistic回归模型的洪涝灾害区划研究 被引量:13

Study on Flood Hazard Risk Zoning Based on GIS and Logistic Regression Model
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摘要 洪涝灾害危险性区划对于洪涝灾害的控制和预防具有重要的意义。以汉江湖北河段沿线为例,在构建洪涝灾害危险性评价指标体系基础上,利用ArcGIS将各项评价指标进行归一化处理,建立研究区的格网并与历史灾害点相连接,得到每个网格内各项评价指标的数据和相应的灾害发生情况。然后,根据二元Logistic回归原理,利用SPSS进行二元逻辑回归分析,从而得出各评价指标与洪涝发生情况的关联性。在此基础上,应用Logistic回归模型确定的危险性概率计算方法,在ArcGIS中绘制研究区的洪涝灾害危险性区划图。将洪涝灾害危险性划分为高危险区、较高危险区、中等危险区、较低危险区和低危险区5个等级,各风险区面积占比依次为8.3%、12.5%、20.6%、19.2%和39.4%。 The flood hazard risk zonation is of great importance to the control and prevention of flood disaster.Taking the catchment area along Han River in Hubei Province as an example,based on the flood hazard risk evaluation index system,ArcGIS was used to normalize each evaluation index.The grid of the research area connected with the historical flood disaster point was accordingly established in order to obtain the statistics of each evaluation index and the corresponding disaster occurrences in each grid.Afterwards,according to the binary Logistic regression principle,SPSS was used for binary logistic regression analysis,so as to obtain the correlation between each evaluation index and the occurrence of flood disaster.On the basis of the above,the risk probability calculation method of Logistic regression model was applied to draw the flood hazard zoning map of the study area in ArcGIS.The results showed that flood hazard risk in the study area coexisted with the two aggregation modes of high and low value.And flood hazard risk was divided into five levels:The highest,higher,moderate,lower and the lowest regions.The area proportion of each risk region was 8.3%,12.5%,20.6%,19.2%and 39.4%,respectively.
作者 王鹏 邓红卫 Wang Peng;Deng Hongwei(School of Resources and Safety Engineering,Central South University,Changsha 410083,China)
出处 《地球科学进展》 CAS CSCD 北大核心 2020年第10期1064-1072,共9页 Advances in Earth Science
基金 国家自然科学基金项目“寒区岩质散体冻胀裂解孕育排土场灾变机理及干预机制研究”(编号:51874352) 中南大学研究生自主探索创新项目“GIS视域下湘江长沙—株洲河段水环境承载力分析研究”(编号:2019zzts992)资助。
关键词 洪涝灾害 危险性区划 汉江 GIS LOGISTIC回归模型 Flood hazard Susceptibility mapping Han River GIS Logistic regression model
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