Table of Contents
Advances in Artificial Intelligence
Volume 2014, Article ID 717803, 11 pages
Research Article

Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India

1Information Technology Department, Dharmsinh Desai University, Nadiad 387001, India
2IICT, Ahmedabad University, Ahmedabad 380009, India

Received 11 January 2014; Accepted 15 May 2014; Published 2 June 2014

Academic Editor: Djamel Bouchaffra

Copyright © 2014 Vipul K. Dabhi and Sanjay Chaudhary. 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.


An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise) component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.