Graph Convolutional Neural Network for Pressure Prediction in Water Distribution Network Sites

Dan Liu, Pei Ma, Shixuan Li (), Wei Lv and Danhui Fang
Additional contact information
Dan Liu: Wuhan University of Technology
Pei Ma: Wuhan University of Technology
Shixuan Li: Wuhan University of Technology
Wei Lv: Wuhan University of Technology
Danhui Fang: Wuhan University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 7, No 18, 2599 pages

Abstract: Abstract The safe operation of water distribution networks (WDNs) is crucial for ensuring the city dwellers’ living standards. Accurate and multi-step predictions of pressure at key sites in WDNs can prevent the occurrence of pipe bursts in the future. Therefore, this study proposes an EMD-Graph-Wavenet-HGSRS model to predict the pressure at several monitoring sites in the WDNs. The LSTC-Tubal method is proposed to repair the abnormal pressure values of the WDNs. Then, the pressure features are enriched by EMD. The predefined adjacent matrix of monitoring points is obtained through the topology of WNDs. And, the enriched pressure features and the predefined adjacent matrix of the monitoring sites are input into the Graph-Wavenet model to predict the pressure values for the next 12 h. In addition, the Graph-Wavenet model is optimized by HGSRS in this study. The results of this study show that the MAE of EMD-Graph-Wavenet decreased by 24.36%, KGE increased by 6.73% compared to Graph-Wavenet. EMD-Graph-Wavenet-HGSRS (optimized by HGSRS) prediction outperforms EMD-Graph-Wavenet model. The MAE of Graph-Wavenet decreased by 40.91% and KGE increased by 11.91% compared to Bi-LSTM. The Bi-LSTM exhibited the best performance among these temporal models, whereas the baseline LSTM had the worst performance. The method proposed in this study can better predict the pressure extremes at each stage of the monitoring sites and provide guidance for the pressure management of actual WDNs.

Keywords: Empirical modal decomposition; Graph convolutional neural network; Hyperparameter search; Spatial and temporal correlation; WDNs pressure (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03788-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:7:d:10.1007_s11269-024-03788-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03788-x

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().