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A temporal and spatial prediction method for urban pipeline network based on deep learning

Author

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  • Liao, Ziyi
  • Liu, Minghui
  • Du, Bowen
  • Zhou, Haijun
  • Li, Linchao

Abstract

Water pipeline is one of the important components of urban infrastructure and plays a key role in residential life. An accurate pressure prediction could help improve the resilience of the system. In recent years, some studies have found that the massive pressure monitoring data have complex temporal and spatial correlations. It issues some new challenges to traditional prediction models. In this study, a pressure prediction method based on spatial–temporal neural network (PP-STNN) is proposed. Before the modeling, the pipeline network is mapped into a graph. In the method, Graph Convolutional Network (GCN) is used to capture the spatial correlation of the pipeline network and Gated Recurrent Unit (GRU) is used to capture the temporal correlation. The proposed method is evaluated using real-world dataset and compared with some benchmark methods. The results show that the proposed method could reach the highest accuracy among all methods for different prediction steps. Moreover, the comparison indicates that simultaneously considering temporal and spatial correlation can contribute to the prediction, especially for multiple steps prediction. Compared with GRU for 3-step, 12-step, and 24-step prediction, the proposed method can improve the Root Mean Square Error (RMSE) by about 19%, 8%, and 8%, respectively. Using deep learning methods, this study can improve the accuracy of the pressure prediction, thus increasing the resilience of the cities and promoting safety and sustainable development in the area.

Suggested Citation

  • Liao, Ziyi & Liu, Minghui & Du, Bowen & Zhou, Haijun & Li, Linchao, 2022. "A temporal and spatial prediction method for urban pipeline network based on deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p2:s0378437122008573
    DOI: 10.1016/j.physa.2022.128299
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    References listed on IDEAS

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    1. Daniel R. Rondinel-Oviedo & Jaime M. Sarmiento-Pastor, 2020. "Water: consumption, usage patterns, and residential infrastructure. A comparative analysis of three regions in the Lima metropolitan area," Water International, Taylor & Francis Journals, vol. 45(7-8), pages 824-846, November.
    2. Zeng, Chao & Ma, Changxi & Wang, Ke & Cui, Zihao, 2022. "Predicting vacant parking space availability: A DWT-Bi-LSTM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    Full references (including those not matched with items on IDEAS)

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