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.

本文介绍了一种针对河流传感器数据的异常检测解决方案，使用图神经网络模型 GDN + 来捕捉传感器之间的复杂时空关系，并针对河流网络数据中的高维数据进行评估，发现 GDN + 比基准方法表现更优越和提供更好的可解释性。同时，本文还介绍了 gnnad 软件。