Comparison of classic time series and artificial intelligence models, various Holt-Winters hybrid models in predicting the monthly flow discharge in Marun dam reservoir

Abstract

In this study, the data at Idenak hydrometric station were used to predict the inflow to Maroun Dam reservoir. For this purpose, different models such as artificial intelligence, Holt-Winters and hybrid models were used. Partial mutual information algorithm was used to determine the input parameters affecting modeling the monthly inflow by artificial intelligence models. According to the Hempel and Akaike information criterion, we introduced the monthly inflow with a 3-month lag, and the temperature with a 1-month lag, with respect to the lowest values of Akaike and the highest values of Hempel as input parameters of artificial intelligence models. The results showed the weak performance of the Holt-Winters model compared to other models and confirmed the superiority of the Holt-adaptive network-based fuzzy inference system (ANFIS) hybrid model with the root-mean-square error of 54 and the coefficient of determination (R2) of 0.83 in the testing process compared to other mentioned models. In addition, the above hybrid models performed better than other models in the test process.