Thunderstorm prediction using soft computing and wavelet

Abstract

Environmental disaster affects the adverse effects on socio-economy, agriculture, biodiversity, industry, human life, and living beings. Thunderstorm is one of such environmental disaster. Thunderstorm produced by cumulonimbus cloud and usually produces heavy rain, gusty winds, and sometimes hail. All types of thunderstorm are dangerous. By using proper methodology of forecasting of thunderstorm, the adverse effect can be reduced. The prediction of thunderstorm is the most difficult work in the weather forecasting because of its temporal and spatial development either physically or dynamically. Lightning associated with thunderstorm which hazards to the wildfire, kills people and other living organisms. Thunderstorm with heavy rain causes flooding. All over the world, several scientist and technological researcher investigated on such severe weather forecasting in prior so that to reduce the disaster. In this view, several researchers have suggested different methodologies such as KNN, RS with SVM, Naïve Bayesian, and ANN with LM algorithm for prediction of thunderstorm. The present research proposed the fuzzy C-means and coif wavelet techniques to yield the improved prediction rate. The proposed research produces an accuracy of 85% in thunderstorm prediction.