Volume 18, Number 68 (summer 2014)                   JWSS 2014, 18(68): 79-88 | Back to browse issues page


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Razavizadeh S, Kavian A, Vafakhah M. Estimation of Suspended Sediment Discharge by Optimal Structure of Artificial Neural Network in Taleghan Watershed. JWSS. 2014; 18 (68) :79-88
URL: http://jstnar.iut.ac.ir/article-1-2778-en.html

Sari Agric. Sci. & Natur. Resour. Univ., Sari, Iran.
Abstract:   (5199 Views)

  Prediction of sediment load transported by rivers is a crucial step in the management of rivers, reservoirs and hydraulic projects. In the present study, in order to predict the suspended sediment of Taleghan river by using artificial neural

network, and recognize the best ANN with the highest accuracy, 500 daily data series of flow discharge on the present day, flow discharge on the past day, flow depth and hydrograph condition (respectively with the average of 13.83 (m3/s), 15.42 (m3/s), 89.83 (cm) and -0.036) as input variables, and 500 daily data series of suspended sediment, as the output of the model were used. The data was related to the period of 1984-2005. 80 different neural networks were developed using different combinations of variables and also changing the number of hidden-layer neurons and threshold functions. The accuracy of the models was then compared by R2 and RMSE. Results showed that the neural network with 3-9-1 structure and input parameters of flow discharge on the present day, flow discharge on the past day and flow depth was superior (R2= 0.97 and RMSE= 0.068) compared to the other structures. The average of the observed data of sediment and that predicted by the optimal model (related to test step) were 1122.802 and 1184.924 (tons per day), respectively.
Full-Text [PDF 210 kb]   (1398 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2014/09/15 | Accepted: 2014/09/15 | Published: 2014/09/15

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