Online Detection of Water Contamination Under Concept Drift
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909748392099840 |
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| author | Li, Jin Malialis, Kleanthis Vrachimis, Stelios G. Polycarpou, Marios M. |
| author_facet | Li, Jin Malialis, Kleanthis Vrachimis, Stelios G. Polycarpou, Marios M. |
| contents | Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder(LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Detection of Water Contamination Under Concept Drift Li, Jin Malialis, Kleanthis Vrachimis, Stelios G. Polycarpou, Marios M. Machine Learning Artificial Intelligence Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder(LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes. |
| title | Online Detection of Water Contamination Under Concept Drift |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.02107 |