An AI-and fuzzy-logic-based framework for climate change adaptation and environmental resilience in Yemen

Abstract

Yemen, a country grappling with political instability and environmental degradation, faces profound challenges due to climate change. This paper introduces a novel Artificial Intelligence (AI) and fuzzy-logic-based framework to enhance regional decision-making for climate change adaptation and environmental resilience. Recognizing the inherent uncertainty and imprecision in environmental systems, a fuzzy multi-objective goal programming model is developed to support optimal policy and resource allocation. The proposed mathematical model considers multiple conflicting objectives (such as minimizing environmental degradation, maximizing adaptation efficiency, and ensuring socio-economic sustainability) subject to resource and infrastructural constraints. Fuzzy membership functions handle ambiguous linguistic inputs such as "moderate rainfall" or "high vulnerability," while AI techniques facilitate knowledge extraction and decision rule generation. The methodology integrates fuzzy logic with goal programming, transforming vague environmental targets into structured, solvable optimization problems. AI modules support rule-based evaluation and scenario testing, enabling the system to simulate climate outcomes under different policy interventions. A numerical illustration using hypothetical yet realistic climate data demonstrates the model's capacity to deliver flexible, adaptive solutions. Results reveal that incorporating fuzzy logic improves solution robustness, especially in prioritizing actions like water conservation, afforestation, and disaster management. The findings validate the model's ability to guide sustainable decision-making in fragile environments such as Yemen. This hybrid approach bridges a significant research gap by offering a practical, adaptive, and uncertainty-tolerant tool for environmental planners and policymakers.