A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring
Copyright © 2024 Elsevier Ltd. All rights reserved..
The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:355 |
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| Enthalten in: |
Journal of environmental management - 355(2024) vom: 20. März, Seite 120496 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liao, Zhenliang [VerfasserIn] |
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| Links: |
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| Themen: |
Bayes' theorem |
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| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jenvman.2024.120496 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369276043 |
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| 245 | 1 | 2 | |a A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring |
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| 500 | |a Date Revised 25.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayes' theorem | |
| 650 | 4 | |a Contamination detection | |
| 650 | 4 | |a Hierarchical clustering | |
| 650 | 4 | |a Online monitoring | |
| 650 | 4 | |a Surface water quality | |
| 650 | 4 | |a Time series increment | |
| 700 | 1 | |a Zhang, Minhao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhiyu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Huijuan |e verfasserin |4 aut | |
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