Improving hybrid modeling using an efficient model for rainfall forecasting

Low-precision rainfall forecasting leads to significant losses in various sectors such as agriculture and the environment. In this regard, the effect of support vector regression (SVR), gene expression programming (GEP) and group data modeling (GMDH) models on improving the performance of the hybrid model was examined, which is based on station rainfall data. Urmia and Isfahan with two different climates were used in the period 1964-2019. In nonlinear section modeling, the third combination with the linear section combination, residuals and observational data in the previous time step had less error, for example in Isfahan station, the rate of RMSE reduction from combination 1 to 3.73 / 62 And the rate of SMAPE reduction from 2 to 3 was equal to 62.79%. The hybrid model had better performance than the stochastic model, so that the amount of RMSE from the stochastic model to the hybrid model with SVR, GEP and GMDH at Urmia station decreased by 79.46, 68.34 and 75.77%, respectively. . The gene expression programming model was less accurate than the other models studied (in Urmia station, the rate of UII reduction from GEP to SVR model was 32.5 and 15.62%, respectively, and in Isfahan station, the rate of increase in Nash coefficient was Sutcliffe from GEP to GMDH was 22.38). The amount of Nash-Sutcliffe coefficient in all three models in Urmia station was higher than Isfahan (the average rate of decrease in Nash-Sutcliffe coefficient from Urmia station to Isfahan was 6.22%) but the value of coefficient in both stations is within acceptable range. Therefore, choosing an efficient model with the right combination in nonlinear modeling will have a significant effect on increasing the efficiency of the hybrid model.