Inteligência Artificial aplicada à prevenção e mitigação de desastres naturais: abordagens técnicas e perspectivas futuras

Abstract

In recent decades, the intensification of natural disasters has generated profound human, environmental, and economic impacts. In this context, Artificial Intelligence (AI) has emerged as a strategic ally for forecasting, monitoring, and responding to extreme events. This work critically and practically examines recent methodologies that apply AI to disaster management, with emphasis on deep neural networks, classification algorithms, predictive modeling, and geospatial analysis. The study integrates climatic, geographic, and socioeconomic data, highlighting reference initiatives such as ATR HarmoniSAR, FloodNet, and GeoDisasterAINet, in addition to contributions from generative AI. The results indicate that, when properly applied, these techniques can enhance prediction accuracy, optimize resource allocation, and strengthen the resilience of vulnerable communities. However, challenges such as data scarcity and quality, implementation costs, technical limitations, and ethical concerns still represent relevant barriers. It is concluded that the advancement of AI in disaster management depends on continuous investment, consistent public policies, and collaboration between academia, government, and society, in order to transform technological potential into practical, transparent, and socially responsible solutions. Keywords: