Novel intelligent TOPSIS variant to rank regions for disaster preparedness

An important facet of disaster mitigation is discovering regions based on their lack of preparedness for combating disaster. Accordingly, organizations can lay down appropriate risk management strategies and guidelines to minimize loss due to disaster. “Technique for order of preference by similarity to ideal solution (TOPSIS)” is a popular multi-criteria decisionmaking (MCDM) method that is deployed for ranking alternatives based on multiple pre-specified criteria. However, the method’s efficiency in ranking region as per multiple criteria for disaster management is far from the ground truth. The authors propose a novel intelligent method HCF-TOPSIS, an extension of traditional TOPSIS, to deliver an efficient ranking mechanism for regional safety assessment of disaster affected regions. HCFTOPSIS capitalizes on entropy (H), closeness (C), and farness (F) metrics to obtain efficient ranking scores of the disaster affected regions. Extensive experimentation validates the claim and proves the superiority of HCFTOPSIS over existing TOPSIS variants. The proposed research presents many benefits, especially to governments and stakeholders, intending to take appropriate actions to contain disasters.减灾防灾的一项重要维度，是基于各地区应对灾害的准备不足情况识别相关区域。据此，相关机构可制定适配的风险管理策略与指南，以最大程度降低灾害带来的损失。逼近理想解排序法（Technique for Order of Preference by Similarity to Ideal Solution，TOPSIS）是一种常用的多准则决策（Multi-Criteria Decision Making，MCDM）方法，用于基于多项预设准则对备选方案进行排序。然而，该方法在基于多准则对灾害管理相关区域进行排序时，其结果与真实情况仍存在较大偏差。为此，本文提出一种新型智能方法HCF-TOPSIS——传统TOPSIS的扩展版本，以构建高效的排序机制，用于受灾地区的区域安全评估。HCF-TOPSIS利用熵（H）、贴近度（C）与疏远度（F）三类指标，计算得到受灾地区的高效排序得分。通过大量实验验证了该方法的有效性，并证明了HCF-TOPSIS相较于现有TOPSIS变体的优越性。本研究成果可为各级政府与利益相关方带来诸多益处，助力其制定适配措施以防控灾害。