School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
School of Public Administration, Hohai University, Nanjing, China.
Environ Sci Pollut Res Int. 2023 Apr;30(19):56786-56801. doi: 10.1007/s11356-023-25609-1. Epub 2023 Mar 16.
Assessment of rural regions' vulnerability to flooding is gaining prominence on a global scale. However, researchers are greatly undermined in their efforts to make a comprehensive assessment owing to the multidimensional and non-linear link between different indicators and flood risk. Thus, a multi-criteria decision-making (MCDM) approach is proposed to assess the multifaceted vulnerability of rural flooding in Khyber Pakhtunkhwa Province, Pakistan. This research presents a hybrid model for flood vulnerability assessment by combining TOPSIS and the entropy weight method. Households' vulnerability to flooding in rural areas is assessed through four components (social, economic, physical, and institutional) and twenty indicators. All indicator weights are derived using the entropy weight method. The TOPSIS method is then used to rank the selected research areas based on their flood vulnerability levels. The ranking results reveal that flood vulnerability is highest in the Nowshehra District, followed by the Charsadda, Peshawar, and D.I. Khan Districts. The weighting results show that physical vulnerability is the most important component, while location of household's house from the river source (< 1 km) is the key indicator for assessing flood vulnerability. A sensitivity analysis is provided to study the impact of indicator's weights on the comprehensive ranking results. The sensitivity results revealed that out of twenty indicators, fourteen indicators had the lowest sensitivity, three indicators were reported with low sensitivity while the other three were considered highly sensitive for flood vulnerability assessment. Our research has the potential to offer policymakers specific guidelines for lowering flood risk in flood-prone areas.
对农村地区洪灾脆弱性的评估在全球范围内受到关注。然而，由于不同指标与洪水风险之间存在多维非线性关系，研究人员在进行全面评估时面临着巨大的困难。因此，本文提出了一种多准则决策（MCDM）方法，以评估巴基斯坦开伯尔-普赫图赫瓦省农村洪灾的多方面脆弱性。本研究通过结合 TOPSIS 和熵权法，提出了一种洪水脆弱性评估的混合模型。通过四个方面（社会、经济、物理和制度）和二十个指标来评估农村地区家庭的洪水脆弱性。所有指标权重均采用熵权法确定。然后，使用 TOPSIS 方法根据洪水脆弱性水平对选定的研究区域进行排名。排名结果表明，洪水脆弱性在瑙谢拉区最高，其次是查沙达、白沙瓦和迪尔区。权重结果表明，物理脆弱性是最重要的组成部分，而家庭房屋与河流源头的距离（<1 公里）是评估洪水脆弱性的关键指标。本文还进行了敏感性分析，以研究指标权重对综合排名结果的影响。敏感性分析结果表明，在二十个指标中，有十四个指标的敏感性最低，三个指标的敏感性较低，另外三个指标的敏感性较高，对洪水脆弱性评估非常重要。本研究可为决策者提供在洪水多发地区降低洪水风险的具体指导。
