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Rainfall Prediction by Using Wavelet General Regression Neural Network

Budi Warsito, Rahmat Gernowo, Aris Sugiharto


In recent many years, several models have been developed to analyze and predict the rainfall. In this paper an attempt has been made to get an alternative model for rainfall prediction by combining two methods, the wavelet technique and Neural Network model. Wavelet transformation has become popular because of its ability to concurrently deal with both the spectral and the interim information contained within time series data. The wavelet decomposition used in this paper is Maximal Overlap Discrete Wavelet Transform (MODWT). In Neural Network layer, General Regression Neural Network (GRNN) is chosen to develop the hybrid model. The combination of MODWT and GRNN is called Wavelet General Regression Neural Network (WGRNN). The model developed is applied to the ten-daily rainfall data in two regions of Central Java, Indonesia.


MODWT, GRNN, prediction, rainfall.

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