Real-Time Water Distribution System Hydraulic Modeling Using Prior Demand Information by Formal Bayesian Approach

AbstractReal-time water distribution system (WDS) hydraulic models are used in water utilities to facilitate the planning and operation of the water distribution system. As a critical model input, spatiotemporally varying nodal water demands significantly affect the performance and applicability of such WDS models. Thus, real-time nodal demands must be calibrated for reliability before their use. The main difficulty for real-time calibration is the lack of observed data sufficient to determine thousands of nodal demands accurately in a network. To address the difficulty, this study proposes a formal Bayesian approach to determine nodal demands in WDS hydraulic modeling by explicitly taking prior water demand information into account and coupling more information to constrain the nodal water demand modeling. Application of the approach on a simple hypothetical network and a field network in a city of eastern Zhejiang Province, China demonstrates that by adding prior information, the nodal demand can be uniquely determined in real time. The approach limits uncertainty propagation and improves the robustness of the real-time model calibration and analysis.