Soft detection model of corrosion leakage risk based on KNN and random forest algorithms
Objective The integrity management of urban gas pipeline networks demands effective risk assessment methods. Corrosion leakage risk assessment necessitates the comprehensive integration of risk assessment factors with various detection operations. Current detection tasks face challenges due to data...
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| Format: | Article |
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Editorial Office of Oil & Gas Storage and Transportation
2024-09-01
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| Series: | You-qi chuyun |
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| Online Access: | https://yqcy.pipechina.com.cn/cn/article/doi/10.6047/j.issn.1000-8241.2024.09.012 |
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| author | Yang YANG Chengzhi LI Xuan DU Xiao YU Shaohua DONG |
| author_facet | Yang YANG Chengzhi LI Xuan DU Xiao YU Shaohua DONG |
| author_sort | Yang YANG |
| collection | DOAJ |
| description | Objective The integrity management of urban gas pipeline networks demands effective risk assessment methods. Corrosion leakage risk assessment necessitates the comprehensive integration of risk assessment factors with various detection operations. Current detection tasks face challenges due to data complexities and significant data deficiencies. Therefore, it is vital to develop a method for predicting and evaluating corrosion leakage risks. Methods Key indicators associated with corrosion leakage risks were selected through a correlation analysis. These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data, based on the K-Nearest Neighbor (KNN) and Random Forest algorithms. Results The model conducted predictions on missing detection data and achieved indirect measurements of key indicators, with a relative error between predicted and measured values staying below 25%, meeting acceptable standards. It effectively forecasts pipeline corrosion leakage risks in instances of missing data, paving the way for additional quantitative assessments. In comparison to prior research, the model displayed enhanced prediction accuracy and reliability, attributed to innovations in extracting multi-factor coupling relationships and algorithm choices. Nonetheless, the emergence of some abnormal data suggested constraints on its predictive capacity under specific circumstances and its dependence on complete and precise data. Consequently, enhancing both the quantity and quality of detection data, along with refining the feature extraction approach for key risk indicators, is anticipated to further boost the accuracy of the model. Conclusion This research enriches the risk prediction theory concerning corrosion leakage in gas pipelines and offers practical benefits in enhancingpipeline operation safety and reliability. Future research efforts should focus on enhancing data acquisition and analysis techniques, optimizing the model structure, and improving the model adaptability and accuracy across various application scenarios. |
| format | Article |
| id | doaj-art-99ff0f3c5e1e4f5890bd069aa0527d7d |
| institution | OA Journals |
| issn | 1000-8241 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Editorial Office of Oil & Gas Storage and Transportation |
| record_format | Article |
| series | You-qi chuyun |
| spelling | doaj-art-99ff0f3c5e1e4f5890bd069aa0527d7d2025-08-20T02:27:01ZzhoEditorial Office of Oil & Gas Storage and TransportationYou-qi chuyun1000-82412024-09-014391064107210.6047/j.issn.1000-8241.2024.09.012yqcy-43-9-1064Soft detection model of corrosion leakage risk based on KNN and random forest algorithmsYang YANG0Chengzhi LI1Xuan DU2Xiao YU3Shaohua DONG4Kunlun Digital Technology Co. LtdKunlun Digital Technology Co. LtdKunlun Digital Technology Co. LtdCollege of Safety and Ocean Engineering, China University of Petroleum (Beijing)College of Safety and Ocean Engineering, China University of Petroleum (Beijing)Objective The integrity management of urban gas pipeline networks demands effective risk assessment methods. Corrosion leakage risk assessment necessitates the comprehensive integration of risk assessment factors with various detection operations. Current detection tasks face challenges due to data complexities and significant data deficiencies. Therefore, it is vital to develop a method for predicting and evaluating corrosion leakage risks. Methods Key indicators associated with corrosion leakage risks were selected through a correlation analysis. These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data, based on the K-Nearest Neighbor (KNN) and Random Forest algorithms. Results The model conducted predictions on missing detection data and achieved indirect measurements of key indicators, with a relative error between predicted and measured values staying below 25%, meeting acceptable standards. It effectively forecasts pipeline corrosion leakage risks in instances of missing data, paving the way for additional quantitative assessments. In comparison to prior research, the model displayed enhanced prediction accuracy and reliability, attributed to innovations in extracting multi-factor coupling relationships and algorithm choices. Nonetheless, the emergence of some abnormal data suggested constraints on its predictive capacity under specific circumstances and its dependence on complete and precise data. Consequently, enhancing both the quantity and quality of detection data, along with refining the feature extraction approach for key risk indicators, is anticipated to further boost the accuracy of the model. Conclusion This research enriches the risk prediction theory concerning corrosion leakage in gas pipelines and offers practical benefits in enhancingpipeline operation safety and reliability. Future research efforts should focus on enhancing data acquisition and analysis techniques, optimizing the model structure, and improving the model adaptability and accuracy across various application scenarios.https://yqcy.pipechina.com.cn/cn/article/doi/10.6047/j.issn.1000-8241.2024.09.012urban gas pipeline networkk-nearest neighbor (knn)random forestleakage risksoft detectionquantitative risk assessment |
| spellingShingle | Yang YANG Chengzhi LI Xuan DU Xiao YU Shaohua DONG Soft detection model of corrosion leakage risk based on KNN and random forest algorithms You-qi chuyun urban gas pipeline network k-nearest neighbor (knn) random forest leakage risk soft detection quantitative risk assessment |
| title | Soft detection model of corrosion leakage risk based on KNN and random forest algorithms |
| title_full | Soft detection model of corrosion leakage risk based on KNN and random forest algorithms |
| title_fullStr | Soft detection model of corrosion leakage risk based on KNN and random forest algorithms |
| title_full_unstemmed | Soft detection model of corrosion leakage risk based on KNN and random forest algorithms |
| title_short | Soft detection model of corrosion leakage risk based on KNN and random forest algorithms |
| title_sort | soft detection model of corrosion leakage risk based on knn and random forest algorithms |
| topic | urban gas pipeline network k-nearest neighbor (knn) random forest leakage risk soft detection quantitative risk assessment |
| url | https://yqcy.pipechina.com.cn/cn/article/doi/10.6047/j.issn.1000-8241.2024.09.012 |
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