Groundwater Contaminant Plume Identification by Using Kalman Filter Technique and Fuzzy Set Theory

Abstract:

A methodology is proposed to identify the contaminant plume based on Kalman filter technique and fuzzy set theory. In this methodology, the Kalman filter technique is adopted to update the composite contaminant plume and the corresponding error covariance matrix by using the sampling points sequentially, and the relationship between error covariance matrix and uncertainty of the plume is then combined to select a new sampling point; the fuzzy set theory in the methodology is introduced to update the weight of the potential source location through the comparison of the updated composite plume and the individual plume. The case study indicates that the proposed methodology is effective to identify the contaminant plume, and the number of sampling points is close to a minimum value.