Daily river flow estimation based on intelligent models, case study: Mahabad River

The correct and accurate estimation of river flow can play an important role in reducing the effects of flood damage. In this research, Gene Expression Programming (GEP) model and Bayesian Network (BN) were used to predict daily flow of Mahabad River in Urmia Lake Basin. Accordingly, four input models with a delay of one to four days used to estimate daily flow at time t+1 over a 23-years period and 75% of data was used to train the models and 25% of the remaining data was used for the test stage. Results showed that the best model in both methods was the input pattern with three-time lags. Also, based on the correlation coefficient (R), Root Mean Square Error (RMSE) and Nash-Sutcliffe (E) coefficient in the test stage of the GEP method with R=0.902, RMSE=2.71(m3s-1) and E=0.812 compared to the BN method with R=0.905, RMSE=2.679(m3s-1( and E=0.817 is more accurate. In general, both methods have acceptable accuracy and are they relatively similar, but because of the simpler modeling, Bayesian Network method can be used as an efficient method for predicting river flow.