Abstract
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.
Abstract (translated)
水分配网络(WDNs)是至关重要的基础设施,污染会带来严重的公共卫生风险。有害物质可能会与氯等消毒剂发生反应,因此监测氯含量对于检测污染物至关重要。然而,氯传感器常常变得不可靠,并且需要频繁校准。这项研究引入了一种名为“双重阈值异常和漂移检测”(AD&DD)的方法,这是一种结合了双阈值漂移检测机制与基于LSTM的变分自编码器(LSTM-VAE),用于实时污染检测的无监督方法。该方法在两个真实的WDNs上进行了测试,并能够有效地识别传感器偏置引起的异常作为概念漂移,并且优于其他方法。此外,还提出了一种去中心化架构,通过在选定节点上部署AD&DD来实现准确的污染检测和定位。
URL
https://arxiv.org/abs/2501.02107