Advancements in Disaster Prediction: A Systematic Review of Earthquake, Flood, and Cyclone Forecasting Techniques

Disaster prediction has made progress through the integration of machine learning and deep learning in various types of natural disasters. This comprehensive review provides an in-depth review of recent advances in earthquake, flood, and storm prediction methods. We describe major methods, including global convolutional LSTM for earthquake prediction, hybrid CNN-LSTM model for flood prediction, and dual-branch spatiotemporal fusion network for cyclone detection. Despite this progress, many challenges remain, such as data integration, model performance, and computational limitations. This review outlines these challenges and examines the benefits of various approaches to address them. New techniques, such as federal work for flood forecasting and deep coordination for storm forecasting, are discussed in detail, including their advantages and disadvantages. We also highlight the need for improved data collection, prioritization, and algorithm selection to increase forecast accuracy and reliability. This review integrates recent research findings to provide a better understanding of current approaches and suggests future research and development directions for damage forecasting.