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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-025-00499-7 |
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