Real-Time Water Quality Modeling with Ensemble Kalman Filter for State and Parameter Estimation in Water Distribution Networks

This study presents a novel approach to real-time water quality state (chlorine concentration) and reaction parameter estimation in water distribution systems (WDSs) using ensemble Kalman filter (EnKF)-based data assimilation techniques. Two different types of EnKF-based methods are used in this study: noniterative restart-EnKF (NIR-EnKF) and iterative restart-EnKF (IR-EnKF). The use of these data assimilation frameworks for addressing key uncertainties in water quality models, such as uncertainty in the source or initial concentration of chlorine and uncertainty in the wall reaction parameter, is studied. The effect of ensemble size, number and location of measurement nodes, measurement error, and noise are also studied extensively in this work. The performance of the proposed methodology is tested on two different water networks: a brushy plains network and a large, citywide WDS, the Bangalore inflow network. The results of the simulation study show that both the NIR-EnKF and IR-EnKF methods are appropriate for dealing with uncertainty in source chlorine concentration, but the IR-EnKF method performs better than the NIR-EnKF method in the case of reaction parameter uncertainty.