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Artificial Intelligence in Disaster Management: Sustainable Response and Recovery

Silvio Andrae (Independent Researcher, Germany)
Copyright: © 2025 |Pages: 114
EISBN13: 9798337325637|DOI: 10.4018/979-8-3693-7483-2.ch004
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Abstract

This chapter examines using artificial intelligence (AI) and deep learning (DL) in disaster management. It describes a paradigm shift towards proactive measures in preventing and managing natural disasters. Traditional, reactive methods often reach their limits. At the same time, AI-based approaches can improve early warning systems and allocate resources more efficiently through the analysis of large, heterogeneous data sets and the ability to recognize complex patterns. The article highlights the application of DL models, such as Convolutional Neural Networks (CNNs), to analyze satellite imagery and their utility in disaster response. Both technical and ethical challenges are discussed, particularly data protection, bias, and transparency in the models. Finally, a framework is presented that provides guidelines for the effective and responsible use of AI in disaster management and promotes long-term sustainability and fairness in this area.
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