Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test
作者： Anita Singh , Anurag Malik , Anil Kumar , Ozgur Kisi
DOI: 10.1007/S12517-018-3614-3
关键词:
摘要: 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 Uttarakhand, India. The observed rainfall runoff data from June 1, 2000, to October 31, 2004, were used for training testing applied models. Before starting process, gamma test (GT) select best combination input variables each model. simulated values CANFIS MLPNN models compared with ones respect root mean squared error (RMSE), Nash-Sutcliffe efficiency (CE), Pearson correlation coefficient (PCC). This study provides a conclusive evidence that shows better accuracy than Therefore, according fitting CANFIS-10 model, present day depends on current previous 2 days studied area.
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