Artificial Intelligence–Based Early Warning Systems and Disaster Management: Global Models and Adaptation Strategies for Türkiye

The effects of climate change have increased the frequency and severity of disasters, rendering traditional disaster management approaches inadequate. In this context, AI-Based Early Warning Systems (AI-EWS) have become a strategic tool for the proactive identification of risks and the optimization of response processes.This study analyzes Emergency Events Database (EM-DAT) data for the 1980–2025 period, examines global AI-EWS models, and proposes adaptable strategies for Türkiye. The findings indicate an increase in floods, storms, and heatwaves, while the accuracy of early warnings for slow-onset disasters like drought remains low. The cases of the USA, Japan, the Netherlands, Bangladesh, and China were investigated, determining that AI, machine learning, and community-based models successfully contribute to disaster management in these countries. The strategy proposed for Türkiye encompasses data integration, AI-based forecasting systems, and the digital integration of volunteer networks. In conclusion, the widespread adoption of AI-EWS in Türkiye presents a technological, ethical, and managerial opportunity for transformation, significantly contributing to the process of reducing disaster risk and increasing climate resilience.