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Linear genetic programming to scour below submerged pipeline

机译：线性遗传编程可探查水下管线

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摘要

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.
机译：如今，遗传编程（GP）在水力数据的预测中引起了研究人员的关注。这项研究提出了线性遗传规划（LGP），它是GP的扩展，作为预测管道下方冲刷深度的替代工具。实验室测量的数据集是从已发表的文献中收集的，并用于建立LGP模型。将拟议的LGP模型与自适应神经模糊推理系统（ANFIS）模型结果进行了比较。观察到的LGP的预测值与实测数据非常吻合，并且比ANFIS和基于回归的水下管道冲刷深度方程更好。

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  • 来源
    《Ocean Engineering》 |2011年第9期|p.995-1000|共6页
  • 作者

    H.Md. Azamathulla; Aytac Guven; Yusuf Kagan Demir;

  • 作者单位

    River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus. Seri Ampangan, 14300 Nibong Tebai Pulau Pinang, Malaysia;

    Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey;

    Department of Civil Engineering, University of Gaziantep, 27310 Gaziantep, Turkey;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    local scour; linear genetic programming; neuro-fuzzy; pipelines;

    机译：当地冲刷;线性遗传规划;神经模糊管道;

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