Title: Geo-hydroclimatological-based estimation of sediment yield by the artificial neural network

Authors: Mohammad Ebrahim Banihabib; Ehsan Emami

Addresses: Department of Irrigation and Drainage Engineering, University College of Abureyhan, University of Tehran, P.O. Box: 3391653755, Tehran, Iran ' Department of Civil and Hydraulic Structures, Moshanir Power Engineering Consultants, Tehran, Iran

Abstract: An artificial neural network (ANN) model is proposed for the estimation of sediment yield in Lake Urmia sub-basins. The number of model parameters were extended as far as possible to all geometric, geological and hydroclimatological parameters of the sub-basin. Also, various ANN structures, learning rules, and transfer functions were examined. The examinations show that extended delta and hyperbolic tangent were the best functions for the proposed ANN model. The best structure for the ANN model is a triangle with two hidden layers, containing five neurons in its first and three neurons in its second hidden layer. The comparison between the proposed and regional analysis models showed a notable increase in the accuracy by using the proposed model. Mean absolute error and the maximum absolute error of the estimation reduced to 2.5% and 3% of those regional analysis models, respectively, and therefore ANN model is recommended for sediment yield estimation.

Keywords: Lake Urmia; suspended sediment yield; artificial neural network; basin parameters; regional analysis; hydroclimatological.

DOI: 10.1504/IJW.2017.083771

International Journal of Water, 2017 Vol.11 No.2, pp.159 - 177

Received: 22 Apr 2015
Accepted: 31 Dec 2015

Published online: 12 Apr 2017 *

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