Estimation of Long-Term Rainfall in Babolsar City by Using the Optimized Gene Expression Programming

Estimation and simulation of precipitation is considered as one of the most issues in the field of hydrology. In this study, for the first time, the long-term rainfall in Babolsar city during a 68 years period from 1951 to 2019 was predicted by using an optimized hybrid artificial intelligence (AI) technique. To end this, the gene expression programming (GEP) model was combined with the wavelet transform. To training the AI models, 70% of the observed values were utilized and 30% of these values were used to testing those models. Additionally, the autocorrelation function (ACF) was applied to identify the most influential lags and then six GEP models were developed by means of these detected lags. The number of optimized genes was selected to be four. In addition, the Multiplication function was introduced as the best linking function of the GEP model. The superior GEP model was introduced through a sensitivity analysis that the correlation coefficient (R) and scatter index (SI) for this model were calculated to be 0.571 and 0.792, respectively. The (t-1), (t-2), (t-12), and (t-36) time series lags were introduced as the most effective input lags. The coif was detected as the best mother wavelet to simulate the target function. The hybrid WGEP model simulated the values of rainfall with acceptable accuracy. In the other words, the wavelet transform enhanced the performance of the GEP model significantly. For instance, the value of variance accounted for (VAF) for the GEP and WGEP models were respectively computed to be 31.710 and 82.064