Application of Data Mining Techniques in Weather Forecasting

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Author(s): ThippaReddy Gadekallu (VIT University, India), Bushra Kidwai (VIT University, India), Saksham Sharma (VIT University, India), Rishabh Pareek (VIT University, India)and Sudheer Karnam (VIT University, India)
Copyright: 2019
Pages: 13
Source title: Sentiment Analysis and Knowledge Discovery in Contemporary Business
Source Author(s)/Editor(s): Dharmendra Singh Rajput (VIT University, India), Ramjeevan Singh Thakur (Maulana Azad National Institute of Technology, India)and S. Muzamil Basha (VIT University, India)
DOI: 10.4018/978-1-5225-4999-4.ch010

Keywords: Business and Organizational Research / Business Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this chapter, the authors investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation, and wind speed. This was carried out using artificial decision tree, naive Bayes, random forest, K-nearest neighbors (IBk) algorithms, and meteorological data collected between 2013 and 2014 from the city of Delhi. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. The results show that given enough case data, data mining techniques can be used for weather forecasting and climate change studies.

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