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对河流网络系统的基于图形神经网络的异常检测

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

概括

一种新的方法,GDN+,通过使用图形神经网络,改善了河流传感器数据中的异常检测. 这种方法提高了实时水质监测的准确性和可解释性.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
传感器技术传感器技术

背景情况:

实时水质监测对水生生态系统和人类社会至关重要.
现场传感器技术对于持续的水质评估至关重要.
由于数据的复杂性和可变性,传感器数据中的异常检测具有挑战性.

研究的目的:

为应对河流网络传感器数据中异常检测的挑战.
提高实时水质监测的准确性和可靠性.
提出一种新的方法来识别传感器读数中的错误模式.

主要方法:

利用图形偏差网络 (GDN),这是一个图形神经网络模型,具有基于图形注意力的预测.
提出了一个增强的异常值标准,GDN+,利用学习的图形结构.
开发了新的基准测试模拟,具有复杂的依赖关系和后续异常.
引入了名为gnnad. 的配套软件.

主要成果:

与基线GDN相比,GDN+在高维度河流网络数据中表现出优异的性能.
拟议的GDN+方法为异常检测结果提供了更好的解释性.
对复杂,现实世界河流网络数据集的其他基准分析方法进行GDN评估.

结论:

GDN+代表了河流传感器网络异常检测的重大进步.
改进的模型为水质数据提供了更准确,更易于解释的洞察力.
这项工作有助于更可靠的实时监测系统对重要的水资源.

关键词:

异常检测检测异常检测复杂的系统复杂的系统.图表注意力预测预测图形偏差网络图形偏差网络图形神经网络的神经网络多变量时间序列.时间空间数据时间空间数据

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