عنوان مقاله [English]
Rainfall-runoff process is physical phenomena that their investigation is very difficult due to effectiveness of different parameters. Various methods have so far introduced to analyze these phenomena. This study has been aimed to investigate performance of wavelet-adaptive neuro-fuzzy inference system (wavelet-ANFIS) and adaptive neuro-fuzzy inference system (ANFIS) for simulation of rainfall-runoff process involved with snow water equivalent (SWE) in Latyan watershed located in Tehran province. For this reason, 92 MODIS images have provided by NASA website during three water years 2003-2005, snow cover area in all images has been extracted and finally SWE values have been calculated for the mentioned years. Also, the rainfall, temperature and discharge data for the mentioned years is available which has been used for modeling. The results showed that wavelet-ANFIS with rainfall, temperature and discharge inputs and 1-day delay these inputs with root mean (RMSE) of 0.006 and coeffficient of determination(R2) of 0.97 had more effeciency than ANFIS by grid partitioning with rainfall, temperature and discharge inputs with RMSE of 0.059 and R2 of 0.62 and ANFIS by subtractive clustering with rainfall, temperature and discharge inputs with RMSE of 0.059 and R2 of 0.65. The results Also showed that SWE involvement causes to increase the accuracy of models.