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Modeling sediment yield using artificial neural network and multiple linear regression methods

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Research Paper 01/09/2013
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Modeling sediment yield using artificial neural network and multiple linear regression methods

Lida Eisazadeh, Reza Sokouti, Mehdi Homaee, Ebrahim Pazira
Int. J. Biosci.3( 9), 116-122, September 2013.
Certificate: IJB 2013 [Generate Certificate]
Key: Artificial neural networkRegressionSediment estimationSediment-retention reservoirs

Abstract

Estimating sediment yield in upstream sub basins of reservoirs is an important issue for designing and operation of water resources structures. In classical methods of predicting sediment yield (e. g. regression models) internal uncertainties are not explicitly taken into consideration. However this model cannot improve understanding the internal relationships between the data extracted and cannot determines the impact of each factor of sediment yield. The use of artificial neural networks modeling for prediction and forecasting variables in sedimentation are easier, cheaper and they begin to solve nonlinear problems. In this study, 25 sub basin of reservoir in West Azerbaijan province, Iran, were selected for estimating sediment yield by using multiple linear regression (MLR) and artificial neural network (ANN) methods. Therefore, 160 data sets of sediment yield have been used in selected sub basins of reservoirs. In ANN method, different combinations of inputs and different kinds of functions were designed with the best model by error back propagation algorithm. Also, in the MLR method, a model established by using different parameters of climatic and geomorphological factors. Some statistics including RMSE and R2 were used to evaluate the performance of applied models. The results indicated the proposed ANN model could well predict the sediment yield with R2 = 0. 86 and RMSE= 0.09 in comparison to the MLR model which it’s R2 and RMSE are 0.64 and 1.41 respectively. In particular, the ANN model had the capability of discovering non-linear relationships of sedimentation using geomorphologic parameters with reasonable precision.

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