Utility of Coactive Neuro-Fuzzy Inference System for Runoff Prediction in Comparison with Multilayer Perception

Modeling of hydrological process is important in view of many uses of water resources. This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at single gauging station evaluation of the use of artificial neural network (ANN) models to forecast daily flows at single gauging station in Gunjvani watershed (Maharashtra, India). Two different neural network models, the multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CAN-FIS) models were developed to predict daily stream flow at gauging station were compared. Different scenarios using various combinations of data sets such as rainfall, meteorological parameters and stream flow were developed and compared for their ability to make flow predictions at gauging station. The input vector selection for both models involved quantification of the statistical properties such as cross-, auto- and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 2860 patterns of input-output vector were divided into two sets: 2002 patterns for training, and the remaining 858 patterns for testing. The best performance based on the correlation coefficient (r), ischolar_main mean square error (RMSE), and mean absolute error (MAE) was achieved by the MLP model with current and antecedent rainfall and antecedent flow as model inputs. The MLP model testing resulted in (R=0.93, RMSE=2.27, MAE=2.52). Similarly, the results also showed that the most accurate daily flow predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Both models performed satisfactorily for flow predictions; however, the MLP model outperformed the CANFIS model. The results show that ANN models are useful tools for forecasting the hydrologic response at gauging points of interest in agricultural watersheds.