A weighting strategy to improve water demand forecasting performance based on spatial correlation between multiple sensors

Abstract

Graph convolutional network (GCN) has been used to capture spatial correlation between multiple sensors for better performance of short-term water demand forecasting, which is essential for the implementation of smart water such as optimal scheduling and anomaly detection. However, the GCN assumes every sensor's importance to be the same and describes the spatial correlation purely from a data perspective. To resolve the two issues that affect prediction accuracy, this study proposes a weighting strategy and develops a spatial correlation-based GCN (SCGCN) prediction model. Self-attention mechanism is used to comprehensively analyze water demand data of every sensor and flow resistance between sensors (i.e., head loss along pipes), generating spatial correlation coefficients with the consideration of hydraulics. Then the coefficients are used as weights to aggregate neighboring sensors’ data, extracting accurate input features for the SCGCN model. The study utilized real monitoring data to develop the SCGCN model and compare it with a traditional artificial neural network (ANN) model. Results show that the SCGCN model can outperform the ANN model, especially for multi-step prediction. The root mean square errors of 30, 45, and 60 min multi-step prediction cases are reduced by 4.4% to 9.2% for different sensors.