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- W2167807629 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." @default.
- W2167807629 created "2016-06-24" @default.
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- W2167807629 date "2014-06-02" @default.
- W2167807629 modified "2023-10-03" @default.
- W2167807629 title "Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India" @default.
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- W2167807629 doi "https://doi.org/10.1155/2014/717803" @default.
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