%0 Journal Article
%T Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India
%A Vipul K. Dabhi
%A Sanjay Chaudhary
%J Advances in Artificial Intelligence
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/717803
%X 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. 1. Introduction An accurate prediction of rainfall is crucial for agriculture based Indian economy. Moreover, it also helps in the prevention of flood, the management of water resources, and generating recommendations related to crop for farmers [1]. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. Moreover, the meteorological parameters needed for the rainfall prediction are complex and nonlinear in nature. Practitioners have applied numerical [2, 3] and statistical [4, 5] models for the rainfall prediction. The Numerical Weather Prediction (NWP) models are deterministic models and approximate complex physical processes for weather prediction. However, the models are not useful for prediction at smaller scale due to inherent limitations of these models to initial conditions and model parameterization. Practitioners have also used autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) techniques for developing a model for the rainfall [6]. However, these approaches were developed based on the assumption of stationarity of the given time series and the independence of the residuals. Moreover, these approaches lack the ability to identify nonlinear patterns and irregularity in the time series. Hence, in recent years, use of different machine learning techniques for modeling and prediction of rainfall has received much attention of practitioners [7]. Hung et al. [8] developed a neural network for 1 to 3 hours ahead forecast of rainfall for Bangkok. They used meteorological parameters (air pressure, relative
%U http://www.hindawi.com/journals/aai/2014/717803/