Prediction of Pipeline Corrosion Rate Based on Cross-Validation Gradient Boosting Decision Tree

YAN Jia; HUANG Yi; WANG Xiaona

doi:10.11973/fsyfh-202111010

Corrosion & Protection > 2021 > 42(11): 68-74. > DOI: 10.11973/fsyfh-202111010

YAN Jia, HUANG Yi, WANG Xiaona. Prediction of Pipeline Corrosion Rate Based on Cross-Validation Gradient Boosting Decision Tree[J]. Corrosion & Protection, 2021, 42(11): 68-74. DOI: 10.11973/fsyfh-202111010

Citation:

PDF (3115 KB)

Prediction of Pipeline Corrosion Rate Based on Cross-Validation Gradient Boosting Decision Tree

1. School of Naval Architecture & Ocean Engineering, Dalian University of Technology, Dalian 116024, China;
2. School of Physics, Dalian University of Technology, Dalian 116024, China

More Information

Received Date: November 30, 2019

Graphical Abstract

Abstract

Abstract

According to the idea of ensemble learning, a prediction model of pipeline corrosion rate was established based on the gradient boosting decision tree algorithm, and its hyper-parameters were optimized by grid search and cross-validation methods. The corrosion test data of a certain oil pipeline was utilized to verify the model and was compared with the results predicted by widely used BP neural network and support vector machine model. The results show that the average absolute percentage error of the gradient boosting decision tree model was 2.25%, which was lower than the 6.03% of the BP neural network and the 7.99% of the support vector machine. It indicated that the gradient boosting decision tree model had higher prediction accuracy and better generalization ability than the BP neural network and the support vector machine. In addition, this model had fine interpretability and could provide a new more practical method for the prediction of pipeline corrosion rate in the future.
- cross validation,
- ensemble learning,
- gradient boosting decision tree (GBDT),
- pipeline corrosion rate,
- prediction model

FullText(HTML)

References (24)

References

[1]	许宏良, 殷苏民. 基于改进BP神经网络优化的管道腐蚀速率预测模型研究[J]. 表面技术, 2018, 47(2):177-181.
[2]	胡松青, 石鑫, 胡建春, 等. 基于BP神经网络的输油管道内腐蚀速率预测模型[J]. 油气储运, 2010, 29(6):448-450, 398.
[3]	LEE K E, AZIZ I B A, JAAFAR J B. Adaptive multilayered particle swarm optimized neural network (AMPSONN) for pipeline corrosion prediction[J]. International Journal of Advanced Computer Science and Applications, 2017, 8(11):499-508.
[4]	毕傲睿, 骆正山, 乔伟, 等. 基于主成分和粒子群优化支持向量机的管道内腐蚀预测[J]. 表面技术, 2018, 47(9):133-140.
[5]	吴庆伟, 王金龙, 张平. 基于FOA-SVM模型的输油管道内腐蚀速率预测[J]. 腐蚀与防护, 2017, 38(9):732-736.
[6]	LIU J C, WANG H T, YUAN Z G. Forecast model for inner corrosion rate of oil pipeline based on PSO-SVM[J]. International Journal of Simulation and Process Modelling, 2012, 7(1/2):74.
[7]	GONG M J, BAI Y, QIN J, et al. Gradient boosting machine for predicting return temperature of district heating system:a case study for residential buildings in Tianjin[J]. Journal of Building Engineering, 2020, 27:100950.
[8]	WANG J D, LI P, RAN R, et al. A short-term photovoltaic power prediction model based on the gradient boost decision tree[J]. Applied Sciences, 2018, 8(5):689.
[9]	罗光华, 陈江, 柯翔敏. 关于期货商品交易价格预测仿真研究[J]. 计算机仿真, 2018, 35(9):421-426.
[10]	丁聪, 倪少权, 吕红霞. 基于梯度提升的城市轨道交通客流量预测分析[J]. 城市轨道交通研究, 2018, 21(9):60-63.
[11]	龚越, 罗小芹, 王殿海, 等. 基于梯度提升回归树的城市道路行程时间预测[J]. 浙江大学学报(工学版), 2018, 52(3):453-460.
[12]	沈夏炯, 张俊涛, 韩道军. 基于梯度提升回归树的短时交通流预测模型[J]. 计算机科学, 2018, 45(6):222-227, 264.
[13]	BEN TAIEB S, HYNDMAN R J. A gradient boosting approach to the Kaggle load forecasting competition[J]. International Journal of Forecasting, 2014, 30(2):382-394.
[14]	PAPADOPOULOS S, KARAKATSANIS I. Short-term electricity load forecasting using time series and ensemble learning methods[C]//2015 IEEE Power and Energy Conference at Illinois (PECI).Champaign, IL, USA:IEEE, 2015:1-6.
[15]	周志华. 机器学习[M]. 北京:清华大学出版社, 2016.
[16]	李航. 统计学习方法[M]. 北京:清华大学出版社, 2012.
[17]	FRIEDMAN J H. Greedy function approximation:a gradient boosting machine[J]. The Annals of Statistics, 2001, 29(5):1189-1232.DOI: 10.1214/aos/1013203451.
[18]	PAN Y, CHEN S, QIAO F, et al. Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees[J]. The Science of the Total Environment, 2019, 660:741-750.
[19]	刘金元, 丁勇, 李涛. 基于梯度提升决策树的航班延误分类预测[J]. 数学的实践与认识, 2018, 48(4):1-7.
[20]	ZHANG Y R, HAGHANI A. A gradient boosting method to improve travel time prediction[J]. Transportation Research Part C:Emerging Technologies, 2015, 58:308-324.
[21]	HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning:data mining, inference, and prediction[M]. 2ed. New York:Springer, 2009.
[22]	颜波, 张磊, 褚学宁. 基于卷积神经网络的用户感知评估建模[J]. 上海交通大学学报, 2019, 53(7):844-851.
[23]	曹善成, 宋笔锋, 殷之平, 等. 基于支持向量机回归的飞行载荷参数识别研究[J]. 西北工业大学学报, 2013, 31(4):535-539.
[24]	BÜHLMANN P, YU B. Boosting with the L2 loss[J]. Journal of the American Statistical Association, 2003, 98(462):324-339.

Cited By

Get Citation

PDF

XML

Article views (10) PDF downloads (5)

Turn off MathJax

Article Contents

Abstract

References

Prediction of Pipeline Corrosion Rate Based on Cross-Validation Gradient Boosting Decision Tree

Abstract

References

Catalog

Export File

Citation

Format

Content