Author(s) :
A. T. R. Krishna Priya, R. Anuja
Conference Name :
7th International Conference on Recent Innovations in Computer and Communication (ICRICC 23)
Abstract :
One of the most potent and devastating types of weather phenomena on Earth are tropical cyclones (TC). A cyclone is a large air mass that revolves in a powerful epicenter of low atmospheric pressure, rotating counterclockwise in northern hemisphere and clockwise in southern hemisphere. In this work, a unique deep learning model for predicting the path of tropical cyclones is proposed. Unlike previous predictions, cyclones have the potential to cause serious harm to both individuals and their possessions. To lessen the adverse impacts of cyclones, better and more precise prediction systems are required. In order to anticipate cyclones, this work proposes an efficient Rain Optimized Convolutional Neural Network (RO CNN). The input image is manipulated and preprocessed using Notch filter. Grey Level Coordination Matrix (GLCM) is utilized for gathering the features from segmented output after the processed image and it is fed into K means clustering technique for segmentation. When compared to other conventional methods, the proposed classifier achieves higher levels of accuracy in classification. In order to validate its effectiveness, suggested ROA CNN based system is implemented in MATLAB platform and accuracy obtained during classification using ROA CNN is about 95.8%.