Flood hazard assessment and zonal prioritization through an LR-bipolar triangular fuzzy hybrid decision-making approach

Introduction Flood risk assessment has become increasingly important in regions vulnerable to climate-induced disasters. This study addresses the need for a robust decision-support framework by proposing a hybrid multi-criteria decision-making (MCDM) model to prioritize flood-prone zones in the Ernakulam district of Kerala, aligning with sustainable development goals focused on climate resilience. Methods The proposed approach employs LR-bipolar triangular fuzzy numbers (LRBTFNs) to effectively capture uncertainty in decision-making. It integrates three well-established MCDM techniques, Preference Selection Index (PSI), Simple Additive Weighting (SAW), and Combinative Distance-based Assessment (CODAS), to ensure a balanced and comprehensive ranking process. The methodology incorporates normalization, aggregation, and defuzzification steps to compute performance scores. Additionally, unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA), are utilized to validate vulnerability patterns and group regional profiles. Results and discussion The results reveal that Kochi, Vypen, and Paravoor are the most vulnerable flood-prone zones, while Kothamangalam is identified as the least susceptible area. The integration of multiple MCDM methods enhances the robustness and reliability of the ranking outcomes, and clustering and PCA analyses further confirm consistent vulnerability trends across regions. The findings provide valuable insights for policymakers and local authorities to implement targeted risk mitigation and planning strategies. Moreover, the study supports Sustainable Development Goal 13 (Climate Action) by promoting resilience and preparedness against climate-induced flood hazards.