Abstarct: Due to the necessity of flow forecasting in hydrological issues, such as determination the flow rate input to reservoirs and also flood forecasting, Streamflow forecasting models have received a lot of attention in hydrology science. In this research, artificial intelligence hybrid models including: ANFIS and GA-ANN have been used for short-term (daily) streamflow forecasting. The aim of this research is to predict the outlet of the Latiyan basin, Tehran province, in the period of 2017-2018. Data used in the study including: hydrometric and meteorological data consist of daily precipitation, temperature and discharge data along with snow-covered (SCA), obtained fusion of sentinel-1 and 2 images. For this purpose, total SCA is obtained from the processing of Sentinel-2 optical satellite images through the approach of indices and classification methods combination. Then, wet snow extracted applying Sentinel-1 image processing, SCA(I). in order to extract the effective snow parameter, the fusion algorithm is applied for Sentinel-1 and 2 integrations. Finally, the artificial intelligence model with the help of the effective snow parameter along with other daily hydrometric and meteorological data including: daily precipitation, temperature and discharge are applied in order to forecast the daily outlet of basin. Also, in order to improve the model performance, the seasonal index has been used to identify streamflow trends and better model training. The results showed that the prediction model using satellite data has improved models performance by R=37% and NASH=50%, which shows the direct effect of the snowmelt parameter on the basin runoff. In addition, the trend of changes in the effective snow parameter has a favorable agreement with the flow trend of the basin, especially in the peak flows. Also, using seasonal information as an input parameter can improve the results of the prediction models, RMSE index improved in GA-ANN and ANFIS to 22% and 26% respectivaly. In addition, the AI method baxsed on fuzzy inference (ANFIS) showed better performance than the developed neural network method (GA-ANN) baxsed on statistical indices.