ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems
Abstract Data-driven models often neglect the underlying physical principles, limiting generalization capabilities in water distribution systems (WDSs). This study presents a novel spatio-temporal graph physics-informed neural network (ST-GPINN) for water quality prediction in WDSs, integrating hydr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-025-00499-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238206222958592 |
|---|---|
| author | Tianwei Mu Feiyu Duan Baokuan Ning Bo Zhou Junyu Liu Manhong Huang |
| author_facet | Tianwei Mu Feiyu Duan Baokuan Ning Bo Zhou Junyu Liu Manhong Huang |
| author_sort | Tianwei Mu |
| collection | DOAJ |
| description | Abstract Data-driven models often neglect the underlying physical principles, limiting generalization capabilities in water distribution systems (WDSs). This study presents a novel spatio-temporal graph physics-informed neural network (ST-GPINN) for water quality prediction in WDSs, integrating hydraulic simulations, physics-informed neural networks (PINNs), and graph neural networks (GNNs) to capture dynamics and graph-based network connectivity while approximating partial differential equations (PDEs). ST-GPINN discretizes WDSs using virtual nodes to enhance spatial granularity, employs an Encoder-Processor-Decoder architecture for predictions. Validated on Network A (a small-scale network with 9 junctions and 11 pipes) and Network B (a real large-scale WDS with 920 junctions and 1032 pipes), ST-GPINN outperforms others, achieving a MAE of 0.0073 mg/L, RMSE of 0.0121 mg/L, and R 2 of 88.91% in Network A, and a MAE of 0.008 mg/L, RMSE of 0.0098 mg/L, and R² of 98.91% in Network B. Its scalability and accuracy highlight ST-GPINN’s potential for water quality predictions. |
| format | Article |
| id | doaj-art-2809874ca9824f6bbafa1edf27db016e |
| institution | Kabale University |
| issn | 2059-7037 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Clean Water |
| spelling | doaj-art-2809874ca9824f6bbafa1edf27db016e2025-08-20T04:01:42ZengNature Portfolionpj Clean Water2059-70372025-08-018111510.1038/s41545-025-00499-7ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systemsTianwei Mu0Feiyu Duan1Baokuan Ning2Bo Zhou3Junyu Liu4Manhong Huang5School of Architecture & Civil Engineering, Shenyang University of TechnologySchool of Architecture & Civil Engineering, Shenyang University of TechnologySchool of Architecture & Civil Engineering, Shenyang University of TechnologySchool of Architecture & Civil Engineering, Shenyang University of TechnologySchool of Architecture & Civil Engineering, Shenyang University of TechnologyCollege of Environmental Science and Engineering, State Environmental Protection Engineering Center for Pollution Treatment and Control in Textile Industry, Donghua UniversityAbstract Data-driven models often neglect the underlying physical principles, limiting generalization capabilities in water distribution systems (WDSs). This study presents a novel spatio-temporal graph physics-informed neural network (ST-GPINN) for water quality prediction in WDSs, integrating hydraulic simulations, physics-informed neural networks (PINNs), and graph neural networks (GNNs) to capture dynamics and graph-based network connectivity while approximating partial differential equations (PDEs). ST-GPINN discretizes WDSs using virtual nodes to enhance spatial granularity, employs an Encoder-Processor-Decoder architecture for predictions. Validated on Network A (a small-scale network with 9 junctions and 11 pipes) and Network B (a real large-scale WDS with 920 junctions and 1032 pipes), ST-GPINN outperforms others, achieving a MAE of 0.0073 mg/L, RMSE of 0.0121 mg/L, and R 2 of 88.91% in Network A, and a MAE of 0.008 mg/L, RMSE of 0.0098 mg/L, and R² of 98.91% in Network B. Its scalability and accuracy highlight ST-GPINN’s potential for water quality predictions.https://doi.org/10.1038/s41545-025-00499-7 |
| spellingShingle | Tianwei Mu Feiyu Duan Baokuan Ning Bo Zhou Junyu Liu Manhong Huang ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems npj Clean Water |
| title | ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems |
| title_full | ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems |
| title_fullStr | ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems |
| title_full_unstemmed | ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems |
| title_short | ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems |
| title_sort | st gpinn a spatio temporal graph physics informed neural network for enhanced water quality prediction in water distribution systems |
| url | https://doi.org/10.1038/s41545-025-00499-7 |
| work_keys_str_mv | AT tianweimu stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems AT feiyuduan stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems AT baokuanning stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems AT bozhou stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems AT junyuliu stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems AT manhonghuang stgpinnaspatiotemporalgraphphysicsinformedneuralnetworkforenhancedwaterqualitypredictioninwaterdistributionsystems |