Predicting Cyclone Michaung's Wrath: Leveraging Deep Learning Technique for Intensity and Track Forecasting

Abstract

Tropical cyclones pose significant threats to coastal regions, necessitating accurate and timely monitoring systems for effective disaster management. In this study, Convolutional Neural Networks (CNN) is used to identify and track the tropical cyclone Michaung using a comprehensive dataset comprising INSAT3D captured INFRARED and RAW Cyclone Imagery over the Indian Ocean from 2012 to 2021. The raw data, sourced from the MOSDAC server, has been meticulously labeled by timestamp and corresponding coordinates on the intensity-time graph of each cyclone directory. Our methodology involves training the CNN model on this extensive dataset and subsequently validating its efficacy by predicting satellite images of cyclone Michaung obtained from the MODIS satellite. The integration of both INSAT3D and MODIS imagery enhances the robustness of our model, providing a more comprehensive understanding of cyclone dynamics. Results indicate a high level of accuracy in cyclone identification, showcasing the potential of CNN techniques in satellite image analysis for cyclone monitoring. The approach not only contributes to the field of meteorology but also establishes a framework for the utilization of diverse satellite datasets in training neural networks for real-time cyclone detection and tracking. This research represents a significant step toward leveraging advanced machine learning techniques to enhance the efficiency and accuracy of tropical cyclone monitoring systems, ultimately aiding in early warning and disaster preparedness efforts.