Evaluating disaster-resilient smart cities: a fuzzy AHP-DEMATEL-TOPSIS analysis of systemic barriers and phase-based technology priorities

Abstract

Smart city technologies are increasingly promoted to enhance disaster resilience in urban areas exposed to natural hazards; however, their implementation remains constrained by multiple interrelated technological, governance, social and economic barriers. This study aims to identify, prioritize and analyze the systemic barriers influencing smart city applications in disaster management, reveal their causal interrelationships, and evaluate the phase-specific suitability of smart city technology clusters through an integrated fuzzy multi-criteria decision-making (MCDM) framework. The study applies hybrid fuzzy multi-criteria-decision-making framework to develop a phase-based taxonomy of smart city technologies for disaster management. A systematic literature review identifies key barriers, which are then weighted using Fuzzy AHP based on expert judgments. Fuzzy DEMATEL is used to uncover causal relationships among barriers and Fuzzy TOPSIS ranks smart city technology clusters by their suitability across disaster management phases. The framework translates qualitative evidence into a structured, causal, and quantitative decision-support approach. Findings show that technological and governance barriers dominate smart city–enabled disaster management, ranking highest in Fuzzy AHP and emerging as key causal drivers in Fuzzy DEMATEL. Fuzzy TOPSIS indicates phase-dependent technology suitability: analytics/AI and digital twins are most effective for mitigation and preparedness, while communication and citizen interaction tools become more critical in response and recovery. Unlike previous studies that primarily examine individual technologies, isolated disaster phases or implementation barriers, this study introduces a phase-based taxonomy and an integrated decision-support framework that explicitly links systemic barriers with the suitability of smart city technology clusters across the disaster management cycle. By combining Fuzzy-based replicable MCDM framework, the study enables quantitative barrier prioritization, causal analysis and phase-specific technology evaluation. The proposed framework is adaptable to different disaster types and regional contexts, supporting comparative analysis and evidence-based planning for disaster-resilient smart cities.