Soft computing and data mining techniques for thunderstorms and lightning prediction: A survey
Kanchan Bala, Dilip Kumar Choubey, Sanchita Paul · 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) · 2017
Thunderstorms are fascinating and elegant event, which occurred frequently all over the world. When sudden rumbling of sound associated with a bolt of lightning flashed across the sky, then thunderstorm is said to be occurring. Lightning is associated with every thunderstorm, which kills more people than hurricanes and tornadoes. Heavy rain from thunderstorm causes extensive loss of property and leads to flooding. Accurate prediction of thunderstorm is a difficult task in weather forecasting due to its temporal extension and spatial either physically or dynamically. Different technological and scientific researchers are carried on forecasting of thunderstorm and lightning in advance to reduce damages. In this regard, there are many different methodologies have been proposed such as statistical, machine learning, numerical weather prediction, weather research forecasting, image processing etc. This paper focuses on the survey of several research papers on the thunderstorm and lightning using soft computing and data mining techniques such as neural network, rough set, support vector machine, fuzzy logic, genetic algorithm, k-means clustering, k-nearest neighbor etc. This paper has been also enclosed the suggestions on the future research direction.