Groundwater level abnormal detection based on correlation analysis

Shifen Zhou; Bin Tian; Hang Xia; Pianpian Chen

doi:10.1117/12.2539409

14 February 2020 Groundwater level abnormal detection based on correlation analysis

Shifen Zhou, Bin Tian, Hang Xia, Pianpian Chen

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301G (2020) https://doi.org/10.1117/12.2539409
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

In groundwater monitoring and management, the groundwater level data is usually analyzed and managed to judge the exploitation of groundwater, which requires the monitoring value to have higher accuracy. However, because the monitoring data is susceptible to many factors such as sensor failure and abnormal signal transmission, the error in the abnormal situation is judged, causing the system to falsely report the leak alarm. Therefore, the calculation model of multi-parameter correlation degree can be established through the analysis of the fluctuation cross-correlation between multiple parameters to improve the efficiency and accuracy of abnormal event analysis. According to the research and analysis, the change trend of groundwater level in the monitoring area is basically consistent with the change trend of rainfall. Pearson correlation analysis method can be used to analyze the correlation between groundwater level change and rainfall to improve the accuracy of groundwater level anomaly detection. When the groundwater level change is intuitively far from other data, and the closeness to rainfall is lower than the set correlation threshold, it can be judged as abnormal.

Citation Download Citation

Shifen Zhou, Bin Tian, Hang Xia, and Pianpian Chen "Groundwater level abnormal detection based on correlation analysis", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301G (14 February 2020); https://doi.org/10.1117/12.2539409

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