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多尺度的时空图形神经网络,用于增强水需求预测
Ang Xu1, Tuqiao Zhang1, Xuanpeng Zhang2
1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Water research
|October 7, 2025
概括
本研究介绍了一个多尺度空间时间图神经网络 (MSTGNN),用于准确的水需求预测 (WDF). MSTGNN通过捕捉水分系统中的多尺度模式和适应性空间关系来改善预测.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 准确的水需求预测 (WDF) 对于有效的水分配系统 (WDS) 管理至关重要.
- 图形神经网络 (GNN) 常用于WDF,但现有的方法在单个时间尺度和静态空间图形方面存在困难.
- 这些局限性阻碍了业绩,特别是在复杂的系统和长期预测方面.
研究的目的:
- 为增强WDF提出一个新的多尺度空间时间图神经网络 (MSTGNN).
- 解决现有GNN在捕捉多尺度时间依赖和适应空间关系方面的局限性.
- 为了提高WDF在复杂的WDS中的准确性和可扩展性.
主要方法:
- 开发了MSTGNN来建模水需求时间序列的等级性质.
- 从精细到粗的时间尺度构建了层次性的时间表征.
- 学习了适应性,规模特定的图形结构,以捕捉动态的传感器间依赖性.
主要成果:
- 与六种最先进的方法相比,MSTGNN在前一天的WDF中表现优异.
- 通过使用现实数据集,在每15分钟间隔预测水需求时达到高准确度.
- 在预测准确性和可扩展性方面显示出显著的改进.
结论:
- 在WDS中MSTGNN有效地建模了多尺度的时空依赖.
- 拟议的方法为准确和可扩展的水需求预测提供了一个强大的解决方案.
- 支持开发用于WDS管理的先进智能应用程序.
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