Use of analytical data and intelligent models in runoff precipitation simulation (Case study: Bazoft basin)

Today, the use of intelligent models in simulating runoff has been widely used in water resources management. In this study, in order to predict the daily flow time series of the Morghak hydrometric station in Karun basin, an intelligent model of artificial neural network combined with wavelet analysis has been used. For this purpose, the ERA-INTRIM observational and analytical precipitation time series for 16 years (1378-1382) was decomposed by wavelet transform into frequency subsets, then each subset separately as input data to the artificial neural network model was introduced. The results showed that the analytical data have a high ability to simulate runoff precipitation models and can be a good alternative to observation data of rainfall stations. Also, according to the results of the wavelet transform technique, it can be effective in improving the performance of the simple ANN model for the Bazoft basin by 38% on a daily scale and 72% on a monthly scale.