HELi,CHENLei,JISha-sha,et al.Abnormal Detection of Continuous Water Level Monitoring Data Based on K-shape Clustering[J].China Water & Wastewater,2023,39(11):56-61.
Abnormal Detection of Continuous Water Level Monitoring Data Based on K-shape Clustering
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第39卷
Number:
第11期
Page:
56-61
Column:
Date of publication:
2023-06-01
- Abstract:
- Due to factors such as the late start of drainage network monitoring and the harsh monitoring environment, the current quality of urban drainage network operation data is not optimistic, which directly affects its effective application. However, abnormal detection, as the first step in the effective application of data, has not been effectively carried out in the drainage system. Based on the K-shape clustering algorithm, an abnormal detection process of drainage monitoring data was proposed. First, the feature sequence was extracted and clustered to determine the sequence describing the overall feature or average feature of the time series, thereby reducing the false positive and false negative rates of abnormal detection. Then, a holistic judgment was made on the identified abnormal sequences to improve the recall rate of abnormal detection algorithms. The experimental results showed that the recall rate and precision rate of the drainage monitoring data abnormal detection algorithm based on K-shape could reach 0.891 7 and 0.812 7 respectively. In addition, through a comparative study with the brute force algorithm (BF), it was found that the use of a fixed-length time series segmentation method would lead to an increase in false positive rates, and its effect was inferior to the K-shape clustering algorithm.
Last Update:
2023-06-01