Efficient Rainfall Prediction Using a Dimensionality Reduction Method

Abstract

An accurate prediction of rainfall is a very important task and has vital effects on human life. The usage of machine learning (ML) in the field of meteorology has provided solutions to improve the rainfall prediction accuracy. In the same direction, this study suggests an efficient methodology for the prediction of rainfall events with the aim of dimensionality reduction. Firstly, we identified most relevant features from the weather dataset which plays a major role in the prediction of a rainfall event using a wrapper-based feature selection (FS) technique. Secondly, principal components analysis (PCA) is integrated with the complete as well as with selected features dataset to reduce the data dimensionality. Finally, a thorough comparative analysis of different ML prediction models is presented with different nature of feature inputs. The performance of classification models improved significantly when using reduced features set. Specially PCA integrated with FS technique provided excellent prediction results.