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

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 pres-sure 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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

A pressure prediction method based on spatial-temporal neural network is proposed in this study, which considers the spatial and temporal correlations of the pipeline network. The results show that this method achieves the highest accuracy among all tested methods, especially for multiple steps prediction.