首页> 外文期刊>Procedia - Social and Behavioral Sciences >Spatial Multi-criteria Analysis (SMCA) for Basin-wide Flood Risk Assessment as a Tool in Improving Spatial Planning and Urban Resilience Policy Making: A Case Study of Marikina River Basin, Metro Manila – Philippines
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Spatial Multi-criteria Analysis (SMCA) for Basin-wide Flood Risk Assessment as a Tool in Improving Spatial Planning and Urban Resilience Policy Making: A Case Study of Marikina River Basin, Metro Manila – Philippines

机译:用于流域范围洪水风险评估的空间多准则分析(SMCA),可作为改进空间规划和城市抗灾力决策的工具:菲律宾马尼拉大都会马里基纳河流域的案例研究

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The Philippines is one of the most natural hazard prone countries in the world, especially for water-related disasters (floods and landslides) triggered by typhoon. This study is motivated by the extreme flooding incidence in Metro Manila and surrounding areas in September 2009 during the most devastating typhoon to hit the country named as Tropical Storm Ondoy. Highly urbanized cities of Metro Manila in the downstream of Marikina River Basin suffered the most and the degradation of the upper part of the basin in Rizal Province has been identified as one of the main causes of such massive flooding. The research employed spatially explicit methods of assessment by using spatial multi-criteria analysis (SMCA). At the first stage, analytical frameworks were developed for Basin-wide Flood and Landslide Risk Assessment that integrate Hazard, Exposure and Vulnerability components. Necessary steps were taken including geodatabase preparation, variables definition, standardization of parameters, weight assignment of indicators, and sensitivity analysis. At later stage, analysis was applied in Arc GIS 9.3 software environment to conduct risk mapping. Identified Very High Flood Risk Areas were also validated by Satellite Images.
机译:菲律宾是世界上最容易发生自然灾害的国家之一,特别是对于台风引发的与水有关的灾害(洪水和山体滑坡)。这项研究的动机是2009年9月马尼拉大都会及周边地区发生特大洪灾,当时最大的一次台风袭击了这个名为热带风暴Ondoy的国家。马里基纳河流域下游的马尼拉大都市化程度最高的城市受灾最大,黎刹省流域上部的退化已被认为是造成大规模洪灾的主要原因之一。该研究通过空间多准则分析(SMCA)采用了空间明确的评估方法。在第一阶段,针对流域范围的洪水和滑坡风险评估开发了分析框架,该框架整合了危害,暴露和脆弱性要素。采取了必要的步骤,包括地理数据库准备,变量定义,参数标准化,指标权重分配和敏感性分析。在稍后阶段,将分析应用于Arc GIS 9.3软件环境中以进行风险映射。识别出的极高洪灾风险区域也通过卫星图像进行了验证。

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