Linear genetic programming to scour below submerged pipeline

被引：65

作者：

Azamathulla, H. Md. ^{[1
]}

Guven, Aytac ^{[2
]}

Demir, Yusuf Kagan ^{[2
]}

机构：

[1] Univ Sains Malaysia, River Engn & Urban Drainage Res Ctr REDAC, Nibong Tebal 14300, Pulau Pinang, Malaysia

[2] Gaziantep Univ, Dept Civil Engn, TR-27310 Gaziantep, Turkey

来源：

OCEAN ENGINEERING | 2011年 / 38卷 / 8-9期

关键词：

Local scour; Linear genetic programming; Neuro-fuzzy; Pipelines; NEURAL-NETWORKS; PREDICTION; DOWNSTREAM;

D O I：

10.1016/j.oceaneng.2011.03.005

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

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. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：995 / 1000

页数：6

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