Predicting Tropical Cyclones

Abstract

This study introduces a predictive framework for tropical cyclone forecasting employing support vector machines (SVM). Through the analysis of diverse meteorological parameters, including sea surface temperature, atmospheric pressure, and wind patterns, the SVM algorithm is trained to recognize intricate patterns associated with cyclone development. The model exhibits robust performance in identifying potential cyclonic formations, showcasing its efficacy in early detection. By leveraging historical data, the SVM-based approach contributes to the advancement of cyclone prediction models. The methodology's accuracy and efficiency make it a valuable tool for bolstering existing forecasting capabilities, providing critical information for disaster preparedness and mitigation strategies. This research underscores the potential of SVM as a reliable tool in tropical cyclone prediction, emphasizing its role in fortifying resilience against these formidable natural phenomena.