In recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. So in this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for performance of river flow forecasting process. In this research, three parameters such as raining, temperature and daily discharge of Lighvanchai basin used for daily river flow forecasting in Lighvan River. Then, for determination optimum Lags of input parameters, is studied correlogram of data. Finally, for study of temperatures effect in forecasting, this processing performed by separate of months. Assessment of prediction results by using various values as Nash-Sutcliff coefficient that showed task that ANFIS model had high exact (CNS = 0.979) and low error (RMSE = 0.041) in prediction and the ANFIS model can be employed successfully in river flow forecasting. Also by assessment of final results determined that temperature in October was affected on prediction and causes exact increase of it.
Nabizadeh, M., Mosaedi, A., & Dehghani, A. A. (2012). Intelligent estimation of stream flow by Adaptive Neuro-Fuzzy Inference System. Water and Irrigation Management, 2(1), 69-80. doi: 10.22059/jwim.2012.25093
MLA
Morteza Nabizadeh; Abolfazl Mosaedi; Amir Ahmad Dehghani. "Intelligent estimation of stream flow by Adaptive Neuro-Fuzzy Inference System", Water and Irrigation Management, 2, 1, 2012, 69-80. doi: 10.22059/jwim.2012.25093
HARVARD
Nabizadeh, M., Mosaedi, A., Dehghani, A. A. (2012). 'Intelligent estimation of stream flow by Adaptive Neuro-Fuzzy Inference System', Water and Irrigation Management, 2(1), pp. 69-80. doi: 10.22059/jwim.2012.25093
VANCOUVER
Nabizadeh, M., Mosaedi, A., Dehghani, A. A. Intelligent estimation of stream flow by Adaptive Neuro-Fuzzy Inference System. Water and Irrigation Management, 2012; 2(1): 69-80. doi: 10.22059/jwim.2012.25093
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