TY  - JOUR
T1  - Applicability of Adaptive Neuro-Fuzzy Inference Systems in Daily Reservoir Inflow Forecasting
AU - Lee, T.S. AU - Karimi-Googhari, S.H. 
JO  - International Journal of Soft Computing
VL  - 6
IS  - 3
SP  - 75
EP  - 84
PY  - 2011
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2011.75.84
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2011.75.84
KW  - clustering method
KW  -Reservoir inflow
KW  -forecasting
KW  -fuzzy inference system
KW  -peak inflows
KW  -floods
AB  - Accurate prediction of reservoir inflows is crucial for optimizing the operations of managing water resources. With emerging new data driven modeling approaches, methods based on neuro-fuzzy are becoming established in academic and practical applications. This study investigates the ability of Adaptive Neuro-Fuzzy Inference System (ANFIS) method to improve the accuracy of daily reservoir inflow forecasting. The subtractive clustering method is used to find the best number of fuzzy rules. A comparison is made between the ANFIS model and the Artificial Neural Network (ANN) model. A wide range of statistics measures are used to evaluate the performance of the models. Based on comparisons, it was revealed that the ANFIS technique could not improve the accuracy of estimations in a small tropical catchment and the ANN performed better, especially in capturing peak inflows.
ER  -