Review of the Development of Machine Learning Application in Tropical Cyclone Prediction

Nowadays, along with the trend that more and more devastating tropical cyclones are happening all over the world, peoples are facing serious threat. Since traditional models have trouble giving more accurate prediction results, ML models are introduced to provide a more effective way. This overview briefly summarizes the history of ML and the causes of TCs, giving some algorithms of ML that were applied to TC prediction. It also included two actual examples of ML methods that had made a success on predicting TCs. Despite the fact that challenges exist in data quality and computational resources, machine learning models have proven to have huge potential in improving the accuracy and efficiency of tropical cyclone predictions. Whats more, this overview provides some possible prospects of the field, too, including model optimization, risk assessment, and interdisciplinary collaboration. These urgently-needed advancements are essential for improving the resilience of coastal rigions abilities to deal with TCs and the disaster caused by it.