Artificial neural network model of multilayer perceptron for prediction of daily discharge suspended sediment load and assessment of factors affecting sediment estimation | ||
| مهندسی و مدیریت آبخیز | ||
| Article 10, Volume 10, Issue 2, June 2018, Pages 249-255 PDF (1.03 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwmse.2017.108115.1215 | ||
| Authors | ||
| Mitra Tanhapour1; Mohammad Ebrahim Banihabib* 2 | ||
| 1MSc Student, Abouraihan Campus, University of Tehran, Tehran, Iran | ||
| 2Associate Professor, Abouraihan Campus, University of Tehran, Tehran, Iran | ||
| Abstract | ||
| Prediction of the sediment load in water resources engineering projects such as flow diversion projects and dam construction is important factor for determining their service life. In this study, a model for estimation of daily sediment discharge was proposed using multilayer perceptron Artificial Neural Network (ANN) model with back-propagation learning algorithm. For this purpose, current day’s discharge (Qt), precipitation, number of day in the year (DOY) and previous day’s discharge (Qt-1) data of Zoghal Bridge station (located on Chalus River) from 1990 to 2009 were used for training, verification and test. Results of testing different combinations of input data sets showed that effective parameters of the model performance are current discharge parameter, antecedent discharge, precipitation and DOY, respectively. This results has a relatively good agreement with standardized coefficients of regression model. Coefficient of determination (R2) and Root Mean Square Error (RMSE) were used to compare the different structures of ANN. Therefore, best network with 3-5-1 architecture and the amounts of R2=0.89 and RMSE=0.02 was obtained by elimination of DOY variable. The performance of ANN model in the prediction of sediment discharge was compared with Sediment Rating Curve (SRC) and Multiple Non-Linear Regression (MNLR) model. The results showed, in the training and test steps, SRC method and ANN model have the best performance, respectively. Furthermore, in the test step, the ANN model performed better results compared to two other methods by increasing R2 about 16%. Generally, the proposed ANN model can be estimated sediment discharge by less calculation time and cost and also with more accuracy. | ||
| Keywords | ||
| Technical Note: Artificial intelligence model; Back-propagation algorithm; Current day’s discharge; Multiple non-linear regression model; sediment rating curve | ||
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