Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under normal conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly scoring method, GDN+, based on the learned graph. To evaluate the model's efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability. We also introduce software called gnnad.

翻译：水是河网的生命线，其水质对维持水生生态系统和人类社会至关重要。水质实时监测越来越依赖原位传感器技术。异常检测对于识别传感器数据中的错误模式至关重要，但由于数据在正常条件下也具有复杂性和多变性，这一任务颇具挑战性。本文针对河网传感器数据的异常检测这一难题提出解决方案，该方案对实现准确持续的监测至关重要。我们采用图神经网络模型——近期提出的图偏差网络（GDN），通过基于图注意力机制的预测来捕获传感器间复杂的时空关联。基于学习得到的图结构，我们提出了一种改进的异常评分方法GDN+。为评估模型效能，我们引入了具有高度复杂依赖结构和多种类型子序列异常的新型基准仿真实验。在此基础上，我们进一步对比分析了GDN基线方法与其他基准方法在复杂真实河网数据上的优劣势。结果表明，GDN+在高维数据中表现优于基线方法，同时具有更优的可解释性。我们还开发了名为gnnad的软件工具包。