Month ahead rainfall forecasting using gene expression programming
Danandeh Mehr, Ali
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In the present study, gene expression programming (GEP) technique was used to develop one-month ahead monthly rainfall forecasting models in two meteorological stations located at a semi-arid region, Iran. GEP was trained and tested using total monthly rainfall (TMR) time series measured at the stations. Time lagged series of TMR samples having weak stationary state were used as inputs for the modeling. Performance of the best evolved models were compared with those of classic genetic programming (GP) and autoregressive state-space (ASS) approaches using coefficient of efficiency (R2) and root mean squared error measures. The results showed good performance (0.53<R2<0.56) for GEP models at testing period. In both stations, the best model evolved by GEP outperforms the GP and are significantly superior to the ASS models.