A Comparative Analysis of Data Mining Methods for Weather Prediction

Abstract

In the field of weather, forecasting is a critical application that has long been considered one of the world's most difficult scientific and technical issues. Data mining is as the practice of discovering previously unknown and practical information and knowledge that is embedded in enormous volumes of unclear, noisy, random, and incomplete data. In this study, however, an examination of many common data mining techniques for weather prediction has been offered. Different type of data mining techniques has been investigate. Also, based on that, weather prediction comparison using different data mining approaches has been investigated. Nevertheless, methodologies like data cleaning, data integration, data selection, data transformation, etc. has been discussed for reaching the best solution of predicting weather by using data mining. Finally, this study illustrates how k-means and decision trees are the most appropriate data mining methods for this use case.