{"title":"Multi-Scale Spatio-Temporal Graph Neural Network for Enhanced Water Demand Forecasting","authors":"Ang Xu, Tuqiao Zhang, Xuanpeng Zhang, Yu Shao, Tingchao Yu, Shipeng Chu, Lijuan Qian","doi":"10.1016/j.watres.2025.124711","DOIUrl":null,"url":null,"abstract":"Accurate Water Demand Forecasting (WDF) is essential for effectively managing the Water Distribution System (WDS). Graph neural networks, which utilize pre-defined spatial graphs to model relationships among sensor nodes, have been widely applied to WDF. Existing methods typically capture temporal dependencies at a single time scale and construct static graphs representing the most dominant spatial relationships. These limitations often impair model performance, particularly under increased graph complexity and extended forecasting horizons. To address the above issues, this study proposes a Multi-scale Spatio-Temporal Graph Neural Network (MSTGNN) tailored to the hierarchical nature of water demand time series. Specifically, MSTGNN captures multi-scale demand patterns by constructing hierarchical temporal representations ranging from fine to coarse time scales. Moreover, it adaptively learns scale-specific graph structures to reflect rich inter-sensor dependencies varying across scales. Extensive experiments on a real-world WDF dataset with 54 sensors demonstrate that MSTGNN achieves superior performance over six state-of-the-art methods in day-ahead WDF at 15-minute intervals. Its strength in modeling multi-scale spatio-temporal dependencies significantly enhances forecasting accuracy and scalability, supporting advanced smart applications in WDS.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"101 1","pages":""},"PeriodicalIF":12.4000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2025.124711","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate Water Demand Forecasting (WDF) is essential for effectively managing the Water Distribution System (WDS). Graph neural networks, which utilize pre-defined spatial graphs to model relationships among sensor nodes, have been widely applied to WDF. Existing methods typically capture temporal dependencies at a single time scale and construct static graphs representing the most dominant spatial relationships. These limitations often impair model performance, particularly under increased graph complexity and extended forecasting horizons. To address the above issues, this study proposes a Multi-scale Spatio-Temporal Graph Neural Network (MSTGNN) tailored to the hierarchical nature of water demand time series. Specifically, MSTGNN captures multi-scale demand patterns by constructing hierarchical temporal representations ranging from fine to coarse time scales. Moreover, it adaptively learns scale-specific graph structures to reflect rich inter-sensor dependencies varying across scales. Extensive experiments on a real-world WDF dataset with 54 sensors demonstrate that MSTGNN achieves superior performance over six state-of-the-art methods in day-ahead WDF at 15-minute intervals. Its strength in modeling multi-scale spatio-temporal dependencies significantly enhances forecasting accuracy and scalability, supporting advanced smart applications in WDS.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.