Fault detection in a real-time monitoring network for water quality in the lagoon of Venice (Italy)
In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimat...
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 50(2004), 11 vom: 10., Seite 51-8 |
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| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Water Pollutants Oxygen S88TT14065 |
| Zusammenfassung: | In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimation algorithms to real-time detection of signal drift in high-frequency observations. Two continuous-discrete recursive estimation schemes, namely the Extended Kalman Filter and the Recursive Prediction Error algorithm, were applied to assuring the quality of the dissolved oxygen (DO) time series, as obtained from the Lagoon of Venice (Italy) during August 2002, through the real-time monitoring network of the Magistrato alle Acque (the Venice Water Authority). Results demonstrate the effectiveness of the methodology in early detection of a probable drift in the DO signal. Comparison of these results with those obtained from the application of a related recursive scheme (a Dynamic Linear Regression procedure) suggests the strong benefits of approaching the problem of on-line data quality control with several (not merely a single) independent such estimation methods |
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| Beschreibung: | Date Completed 12.05.2005 Date Revised 21.11.2013 published: Print Citation Status MEDLINE |
| ISSN: | 0273-1223 |