Enhancing Cyclone Intensity Prediction Using Deep Learning Models with INSAT- 3D IR Imagery

Tropical revolving storms also known as cyclones, hurricanes and or typhoons depending on the geographical location cyclones are severe natural calamities that pose a huge risk to coastal communities and marine shipping. Correct anticipation and measurement of cyclone intensity is important particularly in disaster management. Sometimes, the traditional method of calculating the intensity of cyclones depends more on the interpretation of satellite imagery which is time consuming and usually subjective and hence all the inaccuracies. However, in the recent past Deep learning based analysis for cyclones has proven itself to be very useful, making the process of automating and optimizing cyclone intensity prediction more effective. This review paper aims to review recently developed deep learning techniques in cyclone intensity prediction. The paper also details various analyses of the various deep learning architectures that have been used successfully in cyclone intensity prediction. Finally, the paper gives insight into the cyclone intensity prediction performance by various deep learning models, with comparison of their attributes, pros and cons.