A Systematic Review on the Prediction of Hurricanes by Utilizing Machine Learning Techniques

Abstract

Hurricanes are powerful, large-scale tropical cyclones that form over warm ocean waters in the Atlantic Ocean and Eastern Pacific Ocean. These massive storm systems are characterized by their distinctive circular shape and intense wind speeds.

Objective: To conduct a comprehensive systematic review of artificial intelligence (AI) techniques for hurricane prediction, analysing the current state of technological innovations, methodological approaches, and predictive capabilities in meteorological forecasting.

Background: Hurricanes represent one of the most destructive natural phenomena, causing significant economic and humanitarian impacts globally. Traditional forecasting methods have limitations in accurately predicting hurricane trajectories, intensities, and potential impacts, necessitating advanced computational approaches.

Methodology: In this paper, a systematic review is conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Comprehensive searches were performed across multiple databases including Web of Science, Scopus, IEEE Xplore, and specialized meteorological research repositories.

Conclusion: Artificial intelligence techniques demonstrate transformative potential in hurricane prediction, offering unprecedented accuracy and insights. The integration of advanced computational methods with traditional meteorological approaches presents a promising frontier in natural disaster forecasting and mitigation strategies.