Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor
Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the...
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| Format: | Article |
| Language: | English |
| Published: |
Eastern Macedonia and Thrace Institute of Technology
2015-12-01
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| Series: | Journal of Engineering Science and Technology Review |
| Subjects: | |
| Online Access: | http://www.jestr.org/downloads/Volume8Issue5/fulltext85152015.pdf |
| _version_ | 1811264870247563264 |
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| author | Liu Xin Zhang Shaoliang Zheng Pulin |
| author_facet | Liu Xin Zhang Shaoliang Zheng Pulin |
| author_sort | Liu Xin |
| collection | DOAJ |
| description | Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the
limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data
obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using
the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring
points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to
identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results
show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can
overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier
detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring. |
| first_indexed | 2024-04-12T20:12:07Z |
| format | Article |
| id | doaj.art-11f660594301418081fc4dd9d80f0da4 |
| institution | Directory Open Access Journal |
| issn | 1791-2377 1791-2377 |
| language | English |
| last_indexed | 2024-04-12T20:12:07Z |
| publishDate | 2015-12-01 |
| publisher | Eastern Macedonia and Thrace Institute of Technology |
| record_format | Article |
| series | Journal of Engineering Science and Technology Review |
| spelling | doaj.art-11f660594301418081fc4dd9d80f0da42022-12-22T03:18:13ZengEastern Macedonia and Thrace Institute of TechnologyJournal of Engineering Science and Technology Review1791-23771791-23772015-12-0185110116Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier FactorLiu Xin0Zhang Shaoliang1Zheng Pulin 2School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu 221008, China / School of Medicine Information of Xuzhou Medical College, Xuzhou, Jiangsu 221004, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu 221008, China The University of Central Queensland, 44 Greenhill Road, QLD, AustraliaSpatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring.http://www.jestr.org/downloads/Volume8Issue5/fulltext85152015.pdfSpatial local outlier factorCarbon capture and storageCO2Spatial outlier _______________ |
| spellingShingle | Liu Xin Zhang Shaoliang Zheng Pulin Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor Journal of Engineering Science and Technology Review Spatial local outlier factor Carbon capture and storage CO2 Spatial outlier _______________ |
| title | Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor |
| title_full | Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor |
| title_fullStr | Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor |
| title_full_unstemmed | Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor |
| title_short | Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor |
| title_sort | spatial outlier detection of co2 monitoring data based on spatial local outlier factor |
| topic | Spatial local outlier factor Carbon capture and storage CO2 Spatial outlier _______________ |
| url | http://www.jestr.org/downloads/Volume8Issue5/fulltext85152015.pdf |
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