ST-GPINN:一个时空图物理信息神经网络,用于增强配水系统的水质预测

IF 11.4 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Tianwei Mu, Feiyu Duan, Baokuan Ning, Bo Zhou, Junyu Liu, Manhong Huang
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引用次数: 0

摘要

数据驱动的模型往往忽略了潜在的物理原理,限制了水分配系统(WDSs)的泛化能力。本研究提出了一种新的时空图形物理信息神经网络(ST-GPINN),用于WDSs的水质预测,整合水力模拟、物理信息神经网络(pinn)和图形神经网络(gnn),在近似偏微分方程(PDEs)的同时捕捉动态和基于图形的网络连通性。ST-GPINN使用虚拟节点离散wds来增强空间粒度,采用编码器-处理器-解码器架构进行预测。在网络A(包含9个节点和11个管道的小规模网络)和网络B(包含920个节点和1032个管道的真实大规模水系统)上进行验证,ST-GPINN优于其他网络,网络A的MAE为0.0073 mg/L, RMSE为0.0121 mg/L, R2为88.91%,网络B的MAE为0.008 mg/L, RMSE为0.0098 mg/L, R²为98.91%,其可扩展性和准确性突出了ST-GPINN在水质预测方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems

ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems

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 R2 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.

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来源期刊
npj Clean Water
npj Clean Water Environmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍: npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.
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