使用图形神经网络进行转移学习,用于监测有限的供水网络中的压力估计
Jian Wang1, Guangtao Fu1, Dragan Savic2
1Centre for Water Systems, University of Exeter, Exeter EX4 4QF, United Kingdom.
Water research
|August 29, 2025
概括
这项研究引入了基于图形神经网络的半监督转移学习 (GASTL) 方法,用于精确估计水分网络压力,即使传感器数据有限. GASTL有效地在网络之间传输知识,改善监控能力.
科学领域:
- 环境工程
- 数据科学
- 城市基础设施管理
背景情况:
- 供水网络是城市的关键基础设施,需要严格的监控.
- 目前用于WDN压力估计的软传感方法通常需要来自特定网络的广泛训练数据.
- 现实的监测限制导致WDN压力的准确估计存在差距.
研究的目的:
- 提出一种基于图形神经网络的半监督转移学习 (GASTL) 方法来估计WDN压力.
- 通过在不同水域网络之间实现知识转移,应对有限的监测数据的挑战.
- 提高WDN压力监测的准确性和可扩展性.
主要方法:
- 开发了一种整合异质图神经网络 (HGNN) 的 GASTL 方法,用于节点表示.
- 使用可学习的转移参数来调整源和目标WDN之间的数据分布.
- 纳入图形拉普拉斯规则化以增强空间一致性和估计准确性.
主要成果:
- 在相同拓 (R2=0.911,MAPE=9.15%) 和交叉拓 (R2=0.911,MAPE=9.43%) 的转移中,GASTL实现了高精度.
- 传感器的数量和位置被确定为影响转移学习性能的关键因素.
- 拓变化影响最小,表明数据分布变化比结构差异更有影响.
结论:
- 在现实的监测限制下,GASTL提供了可扩展和高效的WDN压力估计解决方案.
- 转移学习对加强WDN监控和管理具有重大潜力.
- 优化传感器的位置对于最大限度地提高转移学习的效率至关重要.
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