应用地理空间工具评估洪水灾害对印度西孟加拉邦马尔达地区社会脆弱性的影响

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引用次数: 0

摘要

社会脆弱性评估是一个动态过程,因地而异。本研究编制了马尔达地区的社会脆弱性指数(SVI),因为洪水淹没会造成多种影响。洪水淹没层是利用多时遥感数据生成的。洪水淹没层是根据实时合成孔径雷达(SAR)数据制作的。对于社会脆弱性评估,最有效的指标是家庭组成、年龄和性别组成以及贫困人口(在册种姓和在册部落)。经济和教育数据来自《2011 年印度人口普查手册》。所有这些数据都与地理信息系统平台上的地区村庄数据库相结合。应用加权叠加分析方法生成研究地区的社会脆弱性指数,其中使用了多影响因素(MIF)技术来确定影响因素。社会脆弱性指数分为极高(4%)、高(37%)、中等(32%)和低(27%)。社会脆弱性指数正与洪水淹没层进一步交叉,以建立该地区最脆弱地区的数据库。据观察,70 个村庄处于极高区,662 个村庄处于高区,578 个村庄处于中区,479 个村庄处于低区。这项研究将有助于灾害管理者和利益相关者了解研究地区的脆弱情况,同时也说明了地理空间技术在灾害管理中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of geospatial tools in the assessment of Flood hazard impact on social vulnerability of Malda district, West Bengal, India

Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inundation. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inundation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.

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