Sediment load estimation by MLR, ANN, NF and Sediment Rating Curve (SRC) in Rio Chama River

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Sediment load estimation by MLR, ANN, NF and Sediment Rating Curve (SRC) in Rio Chama River

Original Article, C22
 Ghorbani M.A, Hosseini S.H, Fazelifard M.H, Abbasi H.
Journal. Civil Eng. Urban. 3(4):136-141. 2013

ABSTRACT:As the major part of river sediments is suspended sediment load, its estimation has important significance to manage of the water resources and environments. In this study, two conventional models: Sediment Rating Curve (SRC) and Multi Linear Regression (MLR) and two artificial intelligent models Artificial Neural Network (ANN) and Neuro-Fuzzy (NF) are applied to estimate suspended sediment load of the Rio Chama, a major tributary river of the Rio Grande, in the U.S. states of Colorado and New Mexico. Three statistical parameters–coefficient of determination (R²), root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) are used to compare the results of models. The resul­­­­ts showed that ANN using only discharge as input and NF model using both discharge and sediment as inputs have better performance than other two models. Furthermore, in this study, mentioned models are applied to evaluate annual sediment load and the best results have been achieved from NF and ANN respectively. Results of this study may be useful in picking up the most suitable modeling approach for similar studies in other river basins.
Keywords: artificial neural network; neuro-fuzzy; regression analysis; sediment rating curve; suspended sediment load

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