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Estimation of Suspended Sediment Load Using Genetic Expression Programming Original Article, C44 Sheikhalipour Z, Hassanpour F. Journal. Civil Eng. Urban. 3(5):292-299. 2013 ABSTRACT:Accurate estimation of suspended sediment load carried by a natural river is important for river engineering and water resources projects. In recent years, using ­ smart systems to increase accuracy of estimating of river sediments are common. In this study were used the­ Genetic Expression Programming (GEP) in order to estimate suspended sediment load in Sistan River. Root mean square error (RMSE), mean bias error (MBE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. GEP is found that scenario 3 with four function and RMSE=0, MBE=2.69×10-4, R2=1 in train period and RMSE=0, MBE=2.4×10-4 and R2=1 in test period are superior in estimating suspended sediment load as the best accurate model. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.Also ­estimation of suspended sediment load using other AI methods such as Particle Swarm Optimization, Tabu Search in Sistan River are suggested. Keywords: Genetic Expression Programming (GEP), Artificial Intelligence (AI), Statistic Indicators, Suspended Sediment Load. منبع: [Hidden Content] دانلود: [Hidden Content].,44-292-299.pdf J. Civil Eng. Urban.,44-292-299.pdf

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 منبع: [Hidden Content] دانلود: [Hidden Content].,22-136-141.pdf J. Civil Eng. Urban.,22-136-141.pdf