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...
| Main Authors: | , , |
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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 |
| Summary: | 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. |
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| ISSN: | 1791-2377 1791-2377 |