Estimation of Suspended Sediment Load Using Genetic Expression Programming

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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.

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