Tropical Cyclone Intensity Forecasting Using Deep Learning

TL;DR: Deep Learning models for Tropical Cyclone intensity forecasting achieved promising results, with LSTM model demonstrating the best performance.

Abstract: Tropical cyclones can produce devastating effects on humans, animals, and the environment. Globally, it has been a recurring problem across different continents. This has necessi-tated conducting research on different aspects relating to Tropical cyclones. To contribute to this research domain, in this study, three Deep Learning (DL) models were developed to predict Tropical Cyclone (TC) intensity. The study used the Hursat and Bestrack datasets from the United States National Oceanic and Atmospheric Administration and employed Convolutional Neural Networks (CNN), Longest Short-term Memory (LSTM), and a combination of CNN and LSTM (CNN-LSTM) to predict TC intensity. Results obtained from the study show that the LSTM model achieved the best results although the difference between the three models was not wide. Contributions from this study can aid in reducing damages to life and properties associated with ropical Cyclones by improving the prediction of TC intensity.