城市排水管网流量预测多视图时空图神经网络模型

doi:10.11947/j.AGCS.2025.20230329

测绘学报 ›› 2025, Vol. 54 ›› Issue (2): 334-344.doi: 10.11947/j.AGCS.2025.20230329

• 地图学与地理信息 • 上一篇

城市排水管网流量预测多视图时空图神经网络模型

涂伟¹^,²^,³(), 池向沅¹^,²^,³, 赵天鸿¹^,⁴(), 杨剑⁵, 朱世平⁶, 陈德莉⁶

^1.深圳大学广东省城市空间信息工程重点实验室，广东　深圳　518060
^2.深圳大学自然资源部大湾区地理环境监测重点实验室，广东　深圳　518060
^3.深圳大学建筑与城市规划学院，广东　深圳　518060
^4.深圳技术大学大数据与互联网学院，广东　深圳　518057
^5.信息工程大学地理空间信息学院，河南　郑州　450052
^6.无锡航征科技有限公司，江苏　无锡　214135

收稿日期:2023-08-10 发布日期:2025-03-11
通讯作者: 赵天鸿 E-mail:tuwei@szu.edu.cn;zhaotianhong@sztu.edu.cn
作者简介:涂伟（1984—），男，博士，教授，研究方向为城市时空大数据分析方法及应用。　E-mail：tuwei@szu.edu.cn
基金资助:
广东省教育厅创新团队项目(2024KCXTD013);深圳市基础研究重点项目(JCYJ20220818100200001);国家自然科学基金(42401553);KartoBit Research Network(KRN2202GK)

Multi-view spatio-temporal graph convolutional networks model for urban drainage networks flow prediction

Wei TU¹^,²^,³(), Xiangyuan CHI¹^,²^,³, Tianhong ZHAO¹^,⁴(), Jian YANG⁵, Shiping ZHU⁶, Deli CHEN⁶

^1.Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China
^2.Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, MNR, Shenzhen University, Shenzhen 518060, China
^3.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
^4.College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518057, China
^5.School of Geospatial Information, Information Engineering University, Zhengzhou 450052, China
^6.Wuxi Hangzheng Science & Technology Co., Ltd., Wuxi 214135, China

Received:2023-08-10 Published:2025-03-11
Contact: Tianhong ZHAO E-mail:tuwei@szu.edu.cn;zhaotianhong@sztu.edu.cn
About author:TU Wei (1984—), male, PhD, professor, majors in urban spatio-temporal big data analysis methods and applications.　E-mail: tuwei@szu.edu.cn
Supported by:
Program of the Innovation Team of the Department of Education of Guangdong Province(2024KCXTD013);The Key Project of Shenzhen Commission of Science and Technology(JCYJ20220818100200001);The National Natural Science Foundation of China(42401553);KartoBit Research Network(KRN2202GK)

摘要/Abstract

摘要：

城市排水管网的流量是其运行效率和安全的关键指标，准确的流量预测对排水管网运行风险预警、优化其运行效率及规划布局至关重要。水流量不仅受到其自身动力学特性的影响，还与管网的空间结构紧密相关，但传统水流量预测方法较少关注水流在管道之间复杂多维的空间依赖关系。针对这一问题，本文提出了一种基于多视图的时空图网络模型，该模型综合考虑了排水管网的空间邻近性和节点间的属性相似性。分别构建最近邻拓扑视图与流量相似性属性视图，使用时空图卷积网络挖掘流量特征的内在时空依赖，利用注意力机制对多个视图的时空依赖特征进行融合以获得流量预测值。利用某市排水管网历史水流监测数据进行试验，结果表明本文提出的多视图时空图神经网络模型取得了较好的预测性能，多视图对比试验验证了不同视图在模型中起到的贡献。

关键词: 管网流量预测, 多视图, 时空图网络, 图深度学习

Abstract:

Flow of urban drainage networks are critical indicators of their operational efficiency and safety. Accurately forecasting these parameters is crucial for risk mitigation, performance enhancement, and layout planning of the networks. Traditional flow forecasting methods typically overlook the complex multidimensional spatial dependencies between the flows in pipelines. This paper proposes a multi-view spatio-temporal graph convolutional networks model that considers both the spatial proximity and attribute similarity of network nodes. It constructs the nearest neighbor-based graph and the flow similarity-based graph, utilizes spatio-temporal graph convolutional networks to uncover intrinsic dependencies, and applies an attention mechanism to merge features from multiple views for enhanced flow predictions. Experiments with historical flow data from an urban drainage network confirm the superior predictive capabilities of our model, with ablation studies validating the contributions of different views.

Key words: drainage networks flow prediction, multi-view graph, spatio-temporal graph network, graph deep learning

中图分类号:

P208

涂伟, 池向沅, 赵天鸿, 杨剑, 朱世平, 陈德莉. 城市排水管网流量预测多视图时空图神经网络模型[J]. 测绘学报, 2025, 54(2): 334-344.

Wei TU, Xiangyuan CHI, Tianhong ZHAO, Jian YANG, Shiping ZHU, Deli CHEN. Multi-view spatio-temporal graph convolutional networks model for urban drainage networks flow prediction[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(2): 334-344.

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未来时间跨度/min	MAPE	RMSE/（m³/s）
15	0.097	0.075
30	0.102	0.078
45	0.106	0.080
60	0.111	0.083

模型	MAPE				RMSE/（m³/s）
模型	15 min	30 min	45 min	60 min	15 min	30 min	45 min	60 min
ARIMA	0.176	0.238	0.258	0.276	0.107	0.141	0.153	0.165
RFR	0.146	0.161	0.175	0.190	0.090	0.095	0.101	0.110
LSTM	0.155	0.152	0.155	0.149	0.106	0.108	0.109	0.112
AutoEncoder	0.142	0.138	0.146	0.152	0.105	0.104	0.103	0.106
GWNet	0.099	0.118	0.128	0.139	0.075	0.080	0.084	0.089
AGCRN	0.095	0.104	0.118	0.118	0.081	0.085	0.087	0.090
MVSTGCN	0.097	0.102	0.106	0.111	0.075	0.078	0.080	0.083

模型	MAPE				RMSE/（m³/s）
模型	15 min	30 min	45 min	60 min	15 min	30 min	45 min	60 min
MVSTGCN	0.097	0.102	0.106	0.111	0.075	0.078	0.080	0.083
MVSTGCN-P	0.104	0.105	0.111	0.118	0.082	0.082	0.084	0.089
MVSTGCN-S	0.104	0.104	0.108	0.117	0.080	0.081	0.082	0.087

城市排水管网流量预测多视图时空图神经网络模型

Multi-view spatio-temporal graph convolutional networks model for urban drainage networks flow prediction

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