Abstract
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.
Highlights
An accurate prediction of rainfall is crucial for agriculture based Indian economy
We have shown the evolved Postfix-genetic programming (GP) solutions with their mean absolute error (MAE), mean squared error (MSE), and correlation coefficient (CC)
The discrete wavelet transform (DWT) is applied on daily time series of every meteorological variable to decompose the series into several DW subseries [14]
Summary
An accurate prediction of rainfall is crucial for agriculture based Indian economy. It helps in the prevention of flood, the management of water resources, and generating recommendations related to crop for farmers [1]. Practitioners have used autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) techniques for developing a model for the rainfall [6]. These approaches were developed based on the assumption of stationarity of the given time series and the independence of the residuals. These approaches lack the ability to identify nonlinear patterns and irregularity in the time series
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