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 the accurate and continuous monitoring of water quality. 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 threshold criteria for the model, 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：水是河网的生命之源，其水质对维持水生生态系统和人类社会具有至关重要的作用。水质实时监测日益依赖于原位传感器技术。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（即使在正常条件下）的复杂性和多变性，这一任务极具挑战性。本文针对河网传感器数据这一挑战性异常检测任务提出了解决方案，这对水质精确持续监测至关重要。我们采用了图神经网络模型——最近提出的图偏差网络（GDN），该模型利用基于图注意力的预测来捕捉传感器间复杂的时空关系。我们基于学习到的图结构，提出了替代异常阈值准则的GDN+模型。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子序列异常类型的新型基准仿真实验。我们进一步在复杂真实河网数据上，将基线方法GDN与其他基准方法进行对比分析其优劣。结果表明，GDN+在高维数据中优于基线方法，同时具有更强的可解释性。我们还介绍了名为gnnad的软件工具。