Detection of thunderstorms using data mining and image processing

Abstract

Thunderstorm is a sudden electrical expulsion manifested by a blaze of lightening with a muffled sound. It is one of the most spectacular mesoscale weather phenomena in the atmosphere which occurs seasonally. On the other hand, prediction of thunderstorms is said to be the most complicated task in weather forecasting, due to its limited spatial and temporal extension either dynamically or physically. Every thunderstorm produce lightening, this kills more people every year than tornadoes. Heavy rain from thunderstorm leads to flash flooding, and causes extensive loss to property and other living organisms. Different scientific and technological researches are been carried on for the forecasting of this severe weather feature in advance to reduce damages. In this regard, many of the researchers proposed various methodologies like STP model, MOM model, CG model, LM model, QKP model, DBD model and so on for the detection, but neither of them could provide an accurate prediction. The present research adopted clustering and wavelet transform techniques in order to improve the prediction rate to a greater extent. This is the first research study carried on thunderstorm prediction using the clustering and wavelet techniques resulting with higher accuracy. The proposed model yields an average accuracy of 89.23% in the identification of thunderstorm.