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Advances in Meteorology
Volume 2014, Article ID 203545, 15 pages
Research Article

Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation

1Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-Ro, Nowon-Gu, Seoul 139-701, Republic of Korea
2Forecast Research Laboratory, National Institute of Meteorological Research, Korea Meteorological Administration, 45 Gisangcheong-gil, Dongjak-gu, Seoul 156-720, Republic of Korea

Received 16 August 2013; Revised 23 October 2013; Accepted 1 November 2013; Published 6 January 2014

Academic Editor: Sven-Erik Gryning

Copyright © 2014 Jae-Hyun Seo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM), k-nearest neighbors algorithm (k-NN), and variant k-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected were precipitation sensing and accumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average. k-VNN outperformed k-NN, but it was dominated by SVM with polynomial kernel.