A framework for forecasting the hourly nodal water demand and improving the performance of real-time hydraulic models considering model uncertainty

Abstract

The real-time hydraulic model (RTHM) is a key assistive tool in water distribution system (WDS) management, and its performance directly affects assisted decision-making. This study develops a framework to improve the timeliness and accuracy of RTHMs, which includes the following five steps: flow data processing, establishing nodal water demand (NWD) prediction models, node grouping, data assimilation (DA) and uncertainty analysis. Based on the actual network data, the performance of two data processing methods and three machine learning algorithms are, respectively, compared, and the best is selected for modeling. In the establishment of the hourly NWD prediction models, massive data, including flow measurement and data of all 26 input variables on climate, time and social influencing factors are used. It is found that the time features are the most important model input parameter. Application results of actual network prove that the flow data processing method, accurate NWD prediction, node grouping and Kalman filter-based DA method reduce the uncertainty in the RTHM and improve its timeliness and accuracy, so as to obtain the real-time state estimation of the WDS. Accurate NWD estimation (especially in the high-demand period) and combining RTHM with DA have a great influence on the uncertainty reduction in water pressure estimation, although uncertainty is weakened in the propagation process. HIGHLIGHTS A framework to improve the timeliness and accuracy of real-time hydraulic models was established, helping to accurately estimate the real-time status of the water distribution system.; Reliable nodal water demand prediction models were established, with an accuracy of hourly level.; The obtained results improve the understanding of the propagation process and reduction methods of the uncertainty of the WDS hydraulic models.;

Keywords

• data assimilation
• machine learning algorithms
• model uncertainty analysis
• real-time hydraulic model
• real-time state estimation
• water demand prediction models