Graph Neural Networks for State Estimation in Water Distribution Systems: Application of Supervised and Semisupervised Learning-Reference-Cited by-同舟云学术

Graph Neural Networks for State Estimation in Water Distribution Systems: Application of Supervised and Semisupervised Learning

Published:2022-05 Issue:5 Volume:148 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Xing Lu¹^ORCID,Sela Lina²^ORCID

Affiliation:

1. Postdoctoral Fellow, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX 78712. ORCID:

2. Assistant Professor, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, Austin, TX 78712 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001550

Reference57 articles.

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4. Battaglia P. W. et al. 2018. “Relational inductive biases deep learning and graph networks.” Preprint submitted October 17 2018. https://arxiv.org/abs/1806.01261.

5. Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

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