基于卡尔曼滤波和模糊集的地下水污染羽识别

在场地信息不确定性的前提下基于卡尔曼滤波技术和模糊集合理论提出一种地下水污染羽识别的方法.利用卡尔曼滤波技术通过连续作用采样点更新综合污染羽与误差协方差矩阵,并结合误差协方差矩阵和不确定度的关系进行新采样点的选取;引入模糊集理论进行污染羽的模糊对比,以确定污染源的权重.算例研究表明,该方法是一种有效的污染羽识别算法,能够以尽可能少的采样点确定污染源的位置,并较为准确地达到识别污染羽的目的.

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

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.