Self‐Adaptive Calibration of Real‐Time Demand and Roughness of Water Distribution Systems

Abstract

Real‐time simulation of water distribution systems (WDSs) has been applied to water resource problems ranging from engineering optimization design and operation planning and management. However, accurate implementation of real‐time simulation of WDS is still a challenging task due to the limited knowledge of the large amount of varying nodal demands and pipe characteristics over the entire network system. This paper presents a self‐adaptive calibration method based on Kalman filter (KF) that takes advantage of the long‐term monitoring data for dual calibration of nodal demands and pipe roughness. Inferential measurements are introduced to avoid linear assumptions of the WDS system and link the hydraulics of WDS with KF. Hence, it is able to employ KF to solve the nonlinear problems in looped water distribution network. By assimilating the long‐term monitoring data and adapting the calibrated parameters to various operating conditions, the framework can reduce the uncertainties caused by measurement errors and quantify the uncertainties by covariance matrixes. In addition, the presented method can help to identify abnormal WDS conditions. Three case studies have been conducted to illustrate the validity of the proposed method and its applications. The results have shown that the proposed framework is reliable and effective in practical applications.

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A Multi-step Data Assimilation framework to investigate the effect of measurement uncertainty in the reduction of water distribution network model errors

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Flowrate Time Series Processing in Engineering Tools for Water Distribution Networks

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