AI-Driven Disaster Prediction and Early Warning Systems: A Systematic Literature Review

Abstract

Numerous advancements in artificial intelligence drive better accuracy and improved performance of disaster prediction as well as early warning systems for hazards. This review collects and integrates contemporary findings regarding AI management of disasters through machine learning along with deep learning along with data analytics techniques which address natural disasters and human-made emergencies. The paper analyzes how artificial intelligence contributes to earthquake forecasting processes while also providing information regarding flood forecasting and wildfire detection systems and other hazard assessment needs. This research studies how AI technology links with Internet of Things (IoT) and remote sensing systems for conducting real-time disaster surveillance. The discussion includes thorough assessments of important barriers which include issues with data quality together with system limitations and moral concerns. Future researchers can use this study to determine ways that will enhance AI-based disaster resilience strategies.