在水分网中使用图形神经网络与稀疏的监测数据实时预测水质
Zilin Li1, Haixing Liu2, Chi Zhang2
1Department of Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China; Centre for Water Systems, University of Exeter, Exeter EX4 4QF, United Kingdom.
Water research
|December 19, 2023
概括
本研究引入了一个封闭图形神经网络 (GGNN),用于预测配电网络中的饮用水质量. 该模型准确地预测整个网络的水质,甚至在未监测的位置,克服传统方法的局限性.
科学领域:
- 环境工程 环境工程
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 在水分网 (WDNs) 中准确预测水质对于公共卫生至关重要.
- 现有的液压模型在校准和计算需求方面扎,而当前的机器学习模型在未经监控的地点无法预测.
研究的目的:
- 开发一种新的机器学习模型,用于在WDN中实时预测水质.
- 解决现有方法在传感器数据稀缺性和网络复杂性方面的局限性.
主要方法:
- 提出了一个封闭图形神经网络 (GGNN) 模型,集成液压流量和水质数据.
- 在训练期间使用掩盖操作来提高预测准确度.
- 该模型在现实世界WDN上进行了评估.
主要成果:
- 该GGNN模型在整个WDN中实现了准确的水质预测,包括未监测的地点.
- 尽管传感器数据有限,但获得了很高的预测准确度 (MAE = 0.07 mg L-1,MAPE = 10.0%).
- 确定最佳的传感器放置对于提高预测准确性至关重要.
结论:
- 该GGNN模型提供了一个实用和有效的机器学习解决方案,用于WDNs的水质预测.
- 这项研究代表了重大进展,为机器学习在WDN分析中潜在地取代液压模型铺平了道路.
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