RETRACTED: A performance evaluation of hybrid genetic algorithm approach for forecasting tropical cyclone categories

Abstract

One of the most and essential parts of weather forecasting is the prediction of a tropical cyclone. All over the world there are weather prediction stations to analyze the natural disasters for safeguarding the people before they would get any damage. Cyclone is one of the dangerous natural catastrophes that several researchers have undergone research over it, and many technologies were developed to find accurate results for its prediction. Several algorithms were proposed for classifying the cyclone data in terms of latitude, longitude, wind speed, and pressure. Still, it is a significant challenge for most of the researchers for predicting the accurate measurement and observations for cyclonic data. In this paper, a hybrid model is proposed, which is a combination of Genetic Algorithm and XP boost for predicting cyclone severity. The data are collected from the Bay of Bengal Ocean, which has been used in the proposed model for classification. Simulation results show a better improvement in predicting the tropical cyclone categories by using the proposed model. Furthermore, comparison of other existing algorithms with the proposed technique is also discussed.