A novel water quality data analysis framework based on time-series data mining.
The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data.
Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: dengweihui@cigit.ac.cn.
Weihui
W
Deng
Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China. Electronic address: wanggy@ieee.org.
Guoyin
G
Wang
D002681:China; D057225:Data Mining; D004784:Environmental Monitoring; D045483:Rivers; D014867:Water; D014874:Water Pollutants, Chemical; D060753:Water Quality
D014874:Water Pollutants, Chemical; D014867:Water
Anomaly detection; Cloud model; Pattern discovery; Similarity measure; Time-series data mining; Water quality analysis