Recent years have shown a significant increase in the occurrence of floods globally, with an impact on habitation and different sectors of the economy. This, in turn, necessitates the use of different flood mitigation strategies, wherein flood vulnerability assessment plays a significant role. The proposed work presents a methodology that combines vulnerability under physical-environmental and socio-economic domains to assess the overall flood vulnerability at the local self-government level. The methodology was illustrated to the case of Aluva municipality, located on the banks of River Periyar, in Kerala state, India. The spatial variation of hazard inducing factors and population statistics were analysed using Geographic Information System (GIS) tools. The machine learning algorithm, Random Forest, which uses hazard inducing factors as input was implemented for the evaluation of physical-environmental vulnerability. The social vulnerability of the region was analysed using the GIS Multi-criteria decision analysis approach (MCDA), with criteria weights to incorporate the interests of different stakeholders. The critical combinations of the two domains of vulnerability in the assessment of the vulnerability to flood, to have efficient flood management in local self-government was demonstrated in this study and can be made use of for any flood event.

中文翻译：

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用地理空间方法评估地方自治政府的洪水易损性

近年来，全球洪灾发生率显着增加，对居民环境和经济的不同部门产生了影响。反过来，这有必要使用不同的洪水缓解策略，其中洪水易损性评估起着重要作用。拟议的工作提出了一种方法，该方法结合了自然环境和社会经济领域的脆弱性，以评估地方自治级别的总体洪灾脆弱性。该方法以印度喀拉拉邦Periyar河岸的Aluva市为例进行了说明。使用地理信息系统（GIS）工具分析了灾害诱发因素和人口统计数据的空间变化。机器学习算法，随机森林，它使用危害诱因作为输入来评估物理环境脆弱性。使用GIS多标准决策分析方法（MCDA）分析了该地区的社会脆弱性，并采用标准权重来考虑不同利益相关者的利益。这项研究证明了脆弱性两个领域在评估洪水脆弱性中的关键组合，以便在地方自治政府中进行有效的洪水管理，并且可以用于任何洪水事件。