Adaptive Kalman Filtering Scheme for the Simulation of Benzene in Subsurface Environment

作者：Chang Shoou Yuh; Boateng Linkel K^*

来源：Journal of Environmental Engineering, 2012, 138(5): 542-551.

DOI：10.1061/(ASCE)EE.1943-7870.0000500

• Benzene
• Subsurface environment
• Deterministic model
• Stochastic process
• Data assimilation
• Adaptive Kalman filter (AKF)

Accurate information about contaminant plume migration in the subsurface plays an important role in risk assessment and emergency response during site remediation. Rapid emergency response during severe soil contamination can help to reduce the extent of damage and the risk of groundwater contamination. The use of subsurface contaminant transport models, coupled with stochastic data assimilation schemes, can provide accurate prediction of contaminant transport to enhance the reliability of risk assessment in the area of environmental remediation. In this study, a two-dimensional deterministic model is used to simulate the advective and diffusive transport of benzene in the subsurface. A robust adaptive Kalman filter (AKF) is constructed as a stochastic data assimilation scheme to improve the prediction of the benzene contaminant plume. The AKF has been proposed to overcome the limitations of the conventional Kalman filter (KF) by reducing the uncertainties associated with the process and observation noise statistics. The impact of the adaptive filter on the KF performance is examined by comparing model predictions with a simulated true field, which is created by introducing random noise into an observation model. The results show that the KF data assimilation scheme can give a more accurate prediction of the benzene plume than the conventional numerical approach although its prediction accuracy is minimal in comparison to the AKF scheme. The KF scheme reduces the root-mean-square error (RMSE) of the plume estimate from 5.0 to 1.1 mg/L at the end of the 10-day simulation. Furthermore, by constructing the AKF data assimilation scheme, the prediction error of KF reduces from 1.1 to 0.9 mg/L, indicating 18% improvement in prediction accuracy. Also, the results of the sensitivity test suggest that filter stability and accuracy greatly depends on the window size, which must be specified to start the adaptive process. DOI:10.1061/(ASCE)EE.1943-7870.0000500.

• 出版日期2012-5