Mahsa H Kashani - Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

In this study, performance of a feedback neural network, Elman, is evaluated for runoff simulation. Themodel ability is compared with two other intelligent models namely, standalone feedforward Multi-layerPerceptron (MLP) neural network model and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS)model. In this case, daily runoff data of a catchment located at south India were collected. Three statisticalcriteria, correlation coefficient, coefficient of efficiency and the difference of slope of a best -fit line fromobserved-estimated scatter plots to 1:1 line, were applied for comparing the performances of the models.The results showed that ANFIS technique provided significant improvement as compared to Elman andMLP models. ANFIS could be an efficient alternative to artificial neural networks, a computationallyintensive method, for runoff predictions providing at least comparable accuracy. Comparing two neuralnetworks indicated that, unexpectedly, Elman technique has high ability than MLP, which is a powerfulmodel in simulation of hydrological processes, in runoff modeling.

Elman, MLP, ANFIS, Runoff simulation, India