Optimization of ANFIS Network to Simulate Long Term Rainfall in Babolsar City

In this study, the long-term rainfall in Babolsar city was simulated using an optimized hybrid artificial intelligence (AI) model over a 68 years period during 1951 to 2019. The ANFIS network and the wavelet transform combined to develop the hybrid model. Firstly, the effective lags of time series data were identified by using the autocorrelation function (ACF). Six ANFIS models were defined through these lags, Subsequently. The best ANFIS model was detected by conducting a sensitivity analysis. For testing the best ANFIS model, the value of correlation coefficient (R), variance accounted for (VAF), and scatter index (SI) were respectively computed to be 0.612, 37.029, and 0.761. In Addition, results of the models analysis showed that the (t-1), (t-2), (t-12), and (t-36) were identified as the most significant lags. Finally, the superior hybrid model was examined in three decomposition level (DL), revealing that the best results were obtained from the second decomposition level (DL2). In testing mode of the model, the R, VAF, and SI were calculated to be 0.972, 94.455, and 0.266, respectively. Therefore, the simulation results showed that the wavelet transform enhanced the performance of the ANFIS network significantly.