Risk Regionalization Of Flood And Waterlogging In Weifang Based On GIS

With the high speed development, weather disasters have an important effect on restricting the social and economic sustainable development. Which flood and waterlogging disaster is more attractive as the highest frequency of its occurrence and the greatest loss. Flood and waterlogging disaster is a typical complex system, whose formation involves many factors, such as natural, social, economic and so on. Flood and waterlogging disaster risk assessment, as a base work of disaster prevention and reduction can help government and mass take effective measures to deal with floods.According to Principle of multi-criteria Estimation and Technology of the Analysis Hierarchy Process, we established the model of GIS-based Risk regionalization of flood and waterlogging. This model includes hazard factor, hazard inducing environment, disaster bodies and disaster prevention and mitigation capacity, with the meteorological date, statistics yearbook, basic geographic information data, The data of land use composition and the historical disaster date, risk regionalization of flood and waterlogging in Weifang was build, the distribution of risk regionalization was obtained. Based on ArcEngine9.3, complying with the idea of software engineering, the software system of risk regionalization of flood and waterlogging was developed with C#.The risk regionalization of flood and waterlogging in Weifang are classified into five grades, including the lowest risk, the second lowest risk, medium risk, the second highest risk and the highest risk. The results show that the areas of central and northern Zhucheng, southwestern Gaomi and southeastern Anqiu are the highest risk regions; the areas of northeastern Anqiu, southern Changyi are the second highest risk regions; most region of Changle, eastern Qingzhou, northeastern Linqu and western Anqiu are the median risk regions, and the areas of southwestern Qingzhou, southwestern Linqu, northeastern Shouguang and northeastern Changyi are the lowest risk regions. This assessment results match with the reality quite well.The major work of this paper is quantitative weight setting by constructing judgment matrix quantitative with the historical disaster date、the data of land use composition and percentile rank while calculating the disaster bodies index. While specializing social-economic data, this article can generate raster thematic maps which can to a large extent reflect the spatial discrepancy of social-economic data between grids within the administrative region.