An ERT pollution area identification method based on clustering algorithm
WANG Yu-ling1, WANG Meng1,2, YAN Yan1, GONG Shu-lan1, WANG Ming1, XU Ya3
1. School of Information and Electrical Engineering, Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Shandong Jianzhu University, Jinan 250101, China; 2. Faculty of Science & Engineering, Linköping University, Linköping, 58183 Sweden; 3. Chinese Research Academy of Environmental Sciences, Beijing 100012
Abstract：Electrical resistivity tomography (ERT) has been used for pollution monitoring in contaminated sites in recent years because it is low cost and relatively fast. However, ERT monitoring data sets are usually analyzed and processed manually, as a result, a lot of manpower is required and the efficiency and accuracy of the ERT data identification are difficult to guarantee. This represents a strong limitation for the application of ERT monitoring system in the fields. To address this problem, clustering algorithms for ERT data analysis was introduced. A numerical model was used to research the effectiveness of contaminated areas recognition using K-means, fuzzy C-means (FCM), and Gaussian mixture model (GMM). The results showed that the three clustering algorithms identified the contaminated area effectively when the difference in resistivity values between the contaminated area and the background soil was larger than 30%. The recognition accuracy of the K-means and FCM algorithms was better than that of the GMM. Finally, a case of the clustering algorithms in ERT survey of a contaminated site was presented.
王玉玲, 王蒙, 闫岩, 宫淑兰, 汪明, 徐亚. 基于聚类算法的ERT污染区域识别方法[J]. 中国环境科学, 2019, 39(3): 1315-1322.
WANG Yu-ling, WANG Meng, YAN Yan, GONG Shu-lan, WANG Ming, XU Ya. An ERT pollution area identification method based on clustering algorithm. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(3): 1315-1322.
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