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 and early warning systems for hazards. This review gathers and integrates current findings on AI management of disasters through machine learning, deep learning, and data analytics techniques that address natural disasters and human-made emergencies. The paper examines how artificial intelligence contributes to earthquake forecasting while also providing information on flood forecasting, wildfire detection systems, and other hazard assessment needs. This research explores how AI technology connects with the Internet of Things (IoT) and remote sensing systems for real-time disaster monitoring. The discussion includes detailed assessments of key barriers, such as data quality issues, system limitations, and ethical concerns. Future researchers can use this study to identify ways to enhance AI-based disaster resilience strategies.