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Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline. 相似文献
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Gene expression programing (GEP) is used to estimate the suspended sediment yield (SSY) in Euphrates River. SSY is considered to be a function of (i) discharge and (ii) time‐lagged discharge and SSY. The proposed models were trained to extrapolate natural stream data collected from five stations in Middle Euphrates Basin. A detailed sensitivity analysis is done to select the time‐lagged discharge and sediment yield variables. GEP implicitly evaluates the contribution of each independent variable on the fitness of candidate solution and eliminates the variable having no contribution. In this study, all input variables are observed to be included in the proposed GEP models, which prove the significance of each variable. Also, standard and modified sediment rating curves and regression‐based formulae are developed for the five stations. In verification, the estimations of GEP formulae agree well with the measured ones. The GEP models are evaluated by the results of the rating curves and regression formulae. In general, the GEP formulae give better results compared to the rating curves and regression‐based formulae. 相似文献
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Aytac Guven 《Journal of Earth System Science》2009,118(2):137-146
In this study linear genetic programming (LGP), which is a variant of Genetic Programming, and two versions of Neural Networks
(NNs) are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne, PA, USA. Daily flow
rate at present is being predicted based on different time-series scenarios. For this purpose, various LGP and NN models are
calibrated with training sets and validated by testing sets. Additionally, the robustness of the proposed LGP and NN models
are evaluated by application data, which are used neither in training nor at testing stage. The results showed that both techniques
predicted the flow rate data in quite good agreement with the observed ones, and the predictions of LGP and NN are challenging.
The performance of LGP, which was moderately better than NN, is very promising and hence supports the use of LGP in predicting
of river flow data. 相似文献
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Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents linear genetic programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth around a circular pile due to waves in medium dense silt and sand bed. Field measurements were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP models were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at circular piles. The results were tabulated in terms of statistical error measures and illustrated via scatter plots. 相似文献
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