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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5766106/ https://www.ncbi.nlm.nih.gov/pubmed/29329297 http://dx.doi.org/10.1371/journal.pone.0189443 |
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author | Eide, Ingvar Westad, Frank |
author_facet | Eide, Ingvar Westad, Frank |
author_sort | Eide, Ingvar |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5766106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57661062018-01-23 Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring Eide, Ingvar Westad, Frank PLoS One Research Article 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. Public Library of Science 2018-01-12 /pmc/articles/PMC5766106/ /pubmed/29329297 http://dx.doi.org/10.1371/journal.pone.0189443 Text en © 2018 Eide, Westad http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Eide, Ingvar Westad, Frank Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5766106/ https://www.ncbi.nlm.nih.gov/pubmed/29329297 http://dx.doi.org/10.1371/journal.pone.0189443 |
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