Integrated AI-ML framework for disaster lifecycle management: From prediction to recovery
Year: 2025
Type: article
Abstract: This article examines the transformative role of artificial intelligence and machine learning (AI-ML) technologies across the disaster management lifecycle. It shows how these technologies enhance prediction accuracy, optimize resource allocation during emergency response, and improve post-disaster recovery operations. The article synthesizes findings from multiple studies and implementations worldwide, demonstrating how AI-ML systems outperform traditional approaches in early warning systems, emergency resource coordination, damage assessment, and infrastructure restoration. Through systematic analysis of case studies and implementation data, the article identifies both the significant benefits of AI-ML integration and the remaining challenges in areas such as data quality, system integration, ethical considerations, and technical infrastructure requirements. The article concludes with an assessment of future research directions and policy recommendations for maximizing the potential of AI-ML to build more resilient communities and reduce the human and economic impacts of disasters.
Author: Devarajan, Vinodkumar
Institution: DELL (United States)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7085461364
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