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<p>Water Distribution Networks (WDNs) are vital for public health and economic activity, and early contamination detection and source-region prioritization are essential for timely response. Although Internet-of-things sensors and machine learning enable online monitoring, water quality sensors are costly and prone to faults, which can distort measurements and lead to false alarms or missed contamination events. Existing contamination localization methods typically assume reliable, fault-free measurements, an assumption often violated in real-world deployments. Here, we propose a unified data-driven framework that explicitly accounts for sensor unreliability while supporting contamination source-region prioritization. In this study, we focus on the monitoring of chlorine-reactive pollutants such as arsenite as a primary application scenario to evaluate the framework’s performance. The framework jointly integrates sensor fault detection, localization, isolation, faulty signal reconstruction, and contamination source-region prioritization within a single monitoring pipeline. A convolutional autoencoder detects and reconstructs corrupted measurements to recover the underlying clean signal, while a CNN–LSTM model performs fault isolation and supports downstream contamination analysis. An explicit fault–contamination differentiation mechanism is further introduced to distinguish sensor-induced deviations from genuine contamination signatures. Comprehensive evaluations are conducted on three realistic WDNs under tested network layouts, synthetic chlorine-reactive contamination scenarios, and evaluated fault models. The results demonstrate that while pinpointing the exact single node is challenging under sparse sensor coverage, the framework can narrow down likely candidate source regions, achieving up to approximately 80% Top-10 source-region prioritization accuracy. These findings highlight that jointly modeling sensor faults and contamination processes is useful for fault-aware contamination source-region prioritization under degraded sensing conditions.</p>

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Toward trustworthy water quality monitoring under sensor unreliability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Jin Li,
  • Kleanthis Malialis,
  • Demetrios G. Eliades,
  • Marios M. Polycarpou

摘要

Water Distribution Networks (WDNs) are vital for public health and economic activity, and early contamination detection and source-region prioritization are essential for timely response. Although Internet-of-things sensors and machine learning enable online monitoring, water quality sensors are costly and prone to faults, which can distort measurements and lead to false alarms or missed contamination events. Existing contamination localization methods typically assume reliable, fault-free measurements, an assumption often violated in real-world deployments. Here, we propose a unified data-driven framework that explicitly accounts for sensor unreliability while supporting contamination source-region prioritization. In this study, we focus on the monitoring of chlorine-reactive pollutants such as arsenite as a primary application scenario to evaluate the framework’s performance. The framework jointly integrates sensor fault detection, localization, isolation, faulty signal reconstruction, and contamination source-region prioritization within a single monitoring pipeline. A convolutional autoencoder detects and reconstructs corrupted measurements to recover the underlying clean signal, while a CNN–LSTM model performs fault isolation and supports downstream contamination analysis. An explicit fault–contamination differentiation mechanism is further introduced to distinguish sensor-induced deviations from genuine contamination signatures. Comprehensive evaluations are conducted on three realistic WDNs under tested network layouts, synthetic chlorine-reactive contamination scenarios, and evaluated fault models. The results demonstrate that while pinpointing the exact single node is challenging under sparse sensor coverage, the framework can narrow down likely candidate source regions, achieving up to approximately 80% Top-10 source-region prioritization accuracy. These findings highlight that jointly modeling sensor faults and contamination processes is useful for fault-aware contamination source-region prioritization under degraded sensing conditions.