Automatic-Cyclone-Track-Prediction

In India, the frequency and intensity of cyclones pose significant challenges to disaster management. Existing tracking systems often struggle to accurately predict the path of cyclones, leading to ineffective preparedness and response efforts. To address these shortcomings, this capstone project focuses on developing an automatic cyclone tracking system using advanced deep learning models.The proposed system utilizes cutting-edge models such as CNN-GRU and CNN-LSTM, which have not been extensively applied to cyclone tracking in the Indian context. By comparing these models to traditional deep learning approaches, the project aims to improve the accuracy and reliability of cyclone tracking. This advancement is crucial for enhancing disaste management strategies and minimizing the impact of cyclones on vulnerable communities.The significance of this research lies in its potential to use cyclone tracking in India. By leveraging the power of deep learning, the project seeks to provide more precise predictions of cyclone paths, enabling authorities to take proactive measures to protect lives and property. Additionally, the project's focus on the Indian context is crucial, as the country faces unique challenges due to its geographical location and population density.