期刊
OCEAN ENGINEERING
卷 38, 期 8-9, 页码 995-1000出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2011.03.005
关键词
Local scour; Linear genetic programming; Neuro-fuzzy; Pipelines
资金
- Universiti Sains Malaysia [304.PREREDAC.6035262]
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. (C) 2011 Elsevier Ltd. All rights reserved.
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