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Graph Neural Network-based Anomaly Detection For River Network Systems.

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Graph Neural Network-based Anomaly Detection For River Network Systems.

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Graph neural network-based anomaly detection for river network systems.

Katie Buchhorn^1,2, Edgar Santos-Fernandez^1,2, Kerrie Mengersen^1,2

¹Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.

|June 10, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new method, GDN+, improves anomaly detection in river sensor data by using graph neural networks. This approach enhances accuracy and interpretability for real-time water quality monitoring.

Keywords:

Anomaly Detection Complex Systems Graph Attention Forecasting Graph Deviation Network Graph Neural Network Multivariate Time Series Spatio-temporal Data

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Area of Science:

Environmental Science
Data Science
Sensor Technology

Background:

Real-time water quality monitoring is vital for aquatic ecosystems and human societies.
In-situ sensor technology is crucial for continuous water quality assessment.
Anomaly detection in sensor data is challenging due to data complexity and variability.

Purpose of the Study:

To address the challenge of anomaly detection in river network sensor data.
To improve the accuracy and reliability of real-time water quality monitoring.
To present a novel approach for identifying erroneous patterns in sensor readings.

Main Methods:

Utilized the Graph Deviation Network (GDN), a graph neural network model with graph attention-based forecasting.
Proposed an enhanced anomaly threshold criteria, GDN+, leveraging the learned graph structure.

Developed new benchmarking simulations with complex dependencies and subsequence anomalies.

Introduced accompanying software named gnnad.

Main Results:

GDN+ demonstrated superior performance compared to the baseline GDN in high-dimensional river network data.
The proposed GDN+ method offers improved interpretability of anomaly detection results.
Evaluated GDN against other benchmarking methods on complex, real-world river network datasets.

Conclusions:

GDN+ represents a significant advancement in anomaly detection for river sensor networks.
The enhanced model provides more accurate and interpretable insights into water quality data.
This work contributes to more reliable real-time monitoring systems for vital water resources.