Comparison of Assessment of Cyclone Intensity based on Deep Learning from Satellite Data

Cyclones are one of the deadliest regular catastrophes which causes colossal annihilation. Cyclones can be extremely deadly; gaining knowledge of it beforehand is helpful for organizing and making arrangements. Several approaches have been implemented in the past to track cyclones and measure their harshness after eye formation. In order to prevent the catastrophe from cyclones quickly and rapidly, tracking the path of cyclones before the eye formation is required. The foremost goal of the research is to anticipate cyclones before the eye forms. SVM algorithm, CNN model and DenseNet model have been compared based on loss and Root Mean Square Error value. The INSAT-3D dataset is trained using DenseNet169 model. The experimental outcomes of the proposed model reveal an RMSE of 2.1893 and Loss of 1.7534. The proposed methodology provides a Web-App, a User Interface that allows user to upload INSAT-3D IR Satellite Images of Cyclones to predict the intensity of the cyclone being formed and also intimate the user through a notification, which displays the details of the cyclone being formed along with its intensity.