We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Novel Method for Anomaly Detection and Signal Calibration in Water Quality Monitoring of an Urban Water Supply System.
- Authors
Liu, Jincheng; Wu, Di; Mohammed, Hadi; Seidu, Razak
- Abstract
Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to 99.6 % . Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management.
- Subjects
NORWAY; MUNICIPAL water supply; CONVOLUTIONAL neural networks; WATER quality monitoring; SIGNAL detection; BIOINDICATORS; WATER quality
- Publication
Water (20734441), 2024, Vol 16, Issue 9, p1238
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w16091238