FUZZY LOGIC BASED RISK ASSESSMENT MODELS FOR NATURAL DISASTERS

Abstract

Fuzzy Logic Based Risk Assessment Models for Natural Disasters represent a major advancement in mathematical modeling under uncertainty. Natural disasters such as floods, earthquakes, landslides, cyclones, and droughts involve nonlinear, uncertain, and dynamic interactions among environmental and socio-economic variables. Traditional probabilistic models often fail to incorporate linguistic ambiguity and incomplete datasets. Fuzzy set theory provides a structured mathematical framework for representing vagueness using degrees of membership. This review presents a systematic analysis of theoretical foundations, model design strategies, system integration approaches, applications, evaluation techniques, and emerging research trends in fuzzy logic-based disaster risk assessment. The review emphasizes fuzzy sets, membership functions, inference systems, defuzzification techniques, and hybrid artificial intelligence integration. Applications in flood zoning, seismic vulnerability mapping, and landslide modeling are discussed. The study concludes that fuzzy logic remains an effective and evolving tool for uncertainty-based disaster modeling.