Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.
A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5766106?pdf=render |
| _version_ | 1818015233602486272 |
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| author | Ingvar Eide Frank Westad |
| author_facet | Ingvar Eide Frank Westad |
| author_sort | Ingvar Eide |
| collection | DOAJ |
| description | A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring. |
| first_indexed | 2024-04-14T06:55:17Z |
| format | Article |
| id | doaj.art-755da62f11ed47cba9a48bf23eb439ac |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-14T06:55:17Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-755da62f11ed47cba9a48bf23eb439ac2022-12-22T02:06:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01131e018944310.1371/journal.pone.0189443Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.Ingvar EideFrank WestadA pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.http://europepmc.org/articles/PMC5766106?pdf=render |
| spellingShingle | Ingvar Eide Frank Westad Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. PLoS ONE |
| title | Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. |
| title_full | Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. |
| title_fullStr | Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. |
| title_full_unstemmed | Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. |
| title_short | Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring. |
| title_sort | automated multivariate analysis of multi sensor data submitted online real time environmental monitoring |
| url | http://europepmc.org/articles/PMC5766106?pdf=render |
| work_keys_str_mv | AT ingvareide automatedmultivariateanalysisofmultisensordatasubmittedonlinerealtimeenvironmentalmonitoring AT frankwestad automatedmultivariateanalysisofmultisensordatasubmittedonlinerealtimeenvironmentalmonitoring |