Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate
Justin Diehr, Ayorinde Ogunyiola, Oluwabunmi Dada
Abstract
Climate change has significantly increased the frequency and severity of disasters, highlighting the limitations of existing disaster response mechanisms. To address these gaps, this study investigates the potential of integrating artificial intelligence (AI) and machine learning (ML) with Geographic Information Systems (GIS) to enhance disaster management and resilience. This research explored the question: What are the key challenges and opportunities associated with integrating AI, ML, and GIS for disaster preparedness and response? Using a systematic review of 71 empirical studies published between 2012 and 2024, this study identifies eight opportunities, including disaster management and risk assessment, flood risk management, landslide susceptibility prediction, innovative visualization techniques, real-time monitoring, early warning systems and efficiency in data processing and analysis. However, significant challenges remain, including data quality, model interpretability, ethical considerations, and technical limitations. The findings highlight the need for improved data governance, transparent modelling approaches, and enhanced computational frameworks to overcome these barriers. By addressing these challenges, AI, ML, and GIS integration can revolutionize disaster preparedness and response, fostering greater resilience and mitigation in the face of climate change-induced disasters.