Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test

Abstract

In this study, daily rainfall-runoff modeling was done using co-active neuro-fuzzy inference system (CANFIS) and multi-layer perceptron neural network (MLPNN) approaches in the hilly Naula watershed of Ramganga River in Uttarakhand, India. The daily observed rainfall and runoff data from June 1, 2000, to October 31, 2004, were used for training and testing of the applied models. Before starting the modeling process, the gamma test (GT) was used to select the best combination of input variables for each model. The simulated values of runoff from CANFIS and MLPNN models were compared with the observed ones with respect to root mean squared error (RMSE), Nash-Sutcliffe efficiency (CE), Pearson correlation coefficient (PCC). This study provides a conclusive evidence that the CANFIS shows better accuracy than the MLPNN models. Therefore, according to the best fitting CANFIS-10 model, the runoff of the present day depends on rainfall and runoff of current and previous 2 days for the studied area.