基于KPCA-ICS-ELM算法的埋地管线土壤腐蚀深度预测

doi:10.16265/j.cnki.issn 1003-3033.2020.12.014

中国安全科学学报 ›› 2020, Vol. 30 ›› Issue (12): 100-105.doi: 10.16265/j.cnki.issn 1003-3033.2020.12.014

基于KPCA-ICS-ELM算法的埋地管线土壤腐蚀深度预测

李易安, 骆正山教授

西安建筑科技大学管理学院,陕西西安 710055

收稿日期:2020-09-16 修回日期:2020-11-19 出版日期:2020-12-28 发布日期:2021-07-15
作者简介:李易安 (1996—),女,河南商丘人,硕士研究生,主要研究方向为管道腐蚀与防护技术。E-mail:13039877593@163.com。
基金资助:
国家自然科学基金资助(41877527);陕西省社科基金资助(2018S349)。

Soil corrosion depth prediction of buried pipelines based on KPCA-ICS-ELM algorithm

LI Yian, LUO Zhengshan

School of Management, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China

Received:2020-09-16 Revised:2020-11-19 Online:2020-12-28 Published:2021-07-15

摘要/Abstract

摘要： 为掌握油气管线土壤腐蚀情况,保障埋地管线安全运行,通过核主成分分析(KPCA)择优土壤腐蚀因素,获取管线腐蚀的高贡献因素;采用改进布谷鸟搜索算法(ICS)优化极限学习机(ELM)隐含层偏差及阈值,将具有高贡献度的指标作为模型输入,腐蚀深度为输出目标,构建ICS-ELM腐蚀深度预测模型;以陕西省某埋地输油管线实地埋片试验为例,选择8种影响因素,建立埋地输油管线土壤腐蚀指标体系,并进行模拟仿真预测及验证分析。结果表明:ICS比标准布谷鸟搜索算法(CS)提前83代收敛,具有更快的迭代速率;KPCA-ICS-ELM模型预测结果的最低相对误差、均方根误差和希尔不等系数分别低达0.209%、0.228%、0.441%;与其他模型相比,该模型准确度更高。

关键词: 核主成分分析(KPCA), 改进布谷鸟搜索算法(ICS), 极限学习机(ELM), 埋地输油管线, 腐蚀深度

Abstract: In order to study soil corrosion situation of oil and gas pipelines and to ensure safe operation of buried pipelines, best corrosion factors were selected and high contribution factors of soil corrosion were obtained by using KPCA. Then, ICS was adopted to optimize deviation and threshold of ELM hidden layer, and with high contribution factors as input and corrosion depth as output target, ICS-ELM corrosion depth prediction model was established. Finally, taking field test of a buried oil pipeline in Shaanxi Province as an example, 8 influencing factors were selected to establish a soil corrosion index system of buried oil pipelines, and prediction and verification analysis were carried out through simulation. The results show that improved CS algorithm has faster iteration rate, and the lowest relative error, root mean square error and hill inequality coefficient of prediction results of KPCA-ICS-ELM model are 0.209%, 0.228% and 0.441% respectively. It is more stable and accurate compared with other models.

Key words: kernel principal component analysis (KPCA), improved cuckoo search (ICS), extreme learning machine (ELM), buried oil pipeline, corrosion depth

中图分类号:

X935

李易安, 骆正山. 基于KPCA-ICS-ELM算法的埋地管线土壤腐蚀深度预测[J]. 中国安全科学学报, 2020, 30(12): 100-105.

LI Yian, LUO Zhengshan. Soil corrosion depth prediction of buried pipelines based on KPCA-ICS-ELM algorithm[J]. China Safety Science Journal, 2020, 30(12): 100-105.

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基于KPCA-ICS-ELM算法的埋地管线土壤腐蚀深度预测

Soil corrosion depth prediction of buried pipelines based on KPCA-ICS-ELM algorithm

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