提出新方法实时检测供水网污染，应对传感器漂移问题。
Online Detection of Water Contamination Under Concept Drift
- 用双阈值检测+LSTM-VAE模型，无监督识别污染异常。
- 在两个真实供水网中，检测准确率优于现有方法。
- 支持分布式部署，可定位污染发生位置，适合智能水务应用。
供水管网是关键基础设施，污染威胁公共健康。有害物质可能与消毒剂（如氯）反应，因此监测氯含量对发现污染物至关重要。然而，氯传感器常出现漂移，需频繁校准。本文提出双阈值异常与漂移检测（AD&DD）方法，结合双阈值漂移检测机制与基于LSTM的变分自编码器（LSTM-VAE），实现无监督的实时污染检测。在两个真实供水网络上测试表明，该方法能有效识别因传感器偏移引发的概念漂移，并显著优于其他方法。此外，提出的去中心化架构通过在部分节点部署AD&DD，实现污染的精准检测与定位。
原文摘要 · Abstract (English)
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.
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