Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient

Abstract

To reduce the adverse influence of sudden water pollution accidents, it is essential to estimate the unknown contaminant source information (normally including the source location, initial release time, and total release mass) as soon as possible. The ensemble Kalman filter (EnKF) has been proven to be an effective algorithm for such an inverse problem. This paper proposes a new method based on EnKF to identify contaminant source information. The method we called RC-EnKF uses the relation coefficient of concentration instead of timely concentration as a state variable in the assimilation process. The advantage lies in the release source mass that can be decoupled from the parameter group of unknown contaminant information to improve the assimilation speed and reduce interference with the accuracy of assimilation. Two categories of cases are employed for validating the applicability and testing the performance compared with the traditional EnKF method in detail. Sensitivity analyses are carried out with different observation errors, number of observation sites, number of ensemble realizations, and model grid size. The results demonstrate that with the uncertain error of observation data, the RC-EnKF works nicely and shows superiority to the traditional EnKF method reflected in the strong immunity to interference from observation data errors and elevated efficiency with the requirement of fewer observation sites, ensemble realizations, and model grids. It illustrates that the RC-EnKF is a more efficient and robust method for estimating unknown contaminant source information.