Proposed an Intelligent Framework for Effective Disaster Management through the Integration of Artificial Intelligence

Abstract

Urbanization and climate change have contributed to disaster frequency and intensity, making disaster risk management essential. This study seeks to address these issues, including lack of future effect prediction, uncoordinated response, and early identification delays. AI can improve danger management, according to past studies. Practical approach issues like real data validation and system compatibility hinder deployment. This study uses case study analysis and literature evaluation based on the high-level Disaster Framework. The five pillars of this framework—Identify, Protect, Detect, Respond, and Recover—address threats throughout a crisis. After adding AI and Smart Identify, Smart Protect, Smart Detect, Smart Respond, and Smart Recover, the framework became the Disaster Smart Framework. The dataset includes Scopus-indexed journal papers, disaster records, and classic and cutting-edge machine learning model validation approaches. The Hazard Intelligence Framework is shaped by eight key AI elements identified in this study. The proposed work offers the groundwork for smarter, more efficient, and more adaptive catastrophe management systems and provides a framework for researching wider applications and ethical implications. Thus, AI catastrophe risk management technologies could save more lives and reduce socioeconomic impacts.