Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network
1
Future Strategy Department, Chungbuk Innovation Institute of Science & Technology, Chungbuk 28126, Korea
2
Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea
3
Environmental Measurement and Analysis Center, National Institute of Environmental Research, Incheon 22689, Korea
4
Engineering Division, DongMoon ENT Co., Ltd., Seoul 08377, Korea
5
Department of Civil and Environmental Engineering, Hanbat National University, Daejeon 34158, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this manuscript.
Water 2020, 12(9), 2411; https://doi.org/10.3390/w12092411
Received: 14 July 2020
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Revised: 24 August 2020
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Accepted: 25 August 2020
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Published: 27 August 2020
(This article belongs to the Section Water Resources Management, Policy and Governance)
It is essential to monitor water quality for river water management because river water is used for various purposes and is directly related to the health and safety of a population. Proper network installation and removal is an important part of water quality monitoring and network operation efficiency. To do this, cluster analysis based on calculated similarity between measuring stations can be used. In this study, we measured the similarities between 12 water quality monitoring stations of the Bukhan River. River water quality data always have a station-dependent time lag because water flows from upstream to downstream; therefore, we proposed a Dynamic Time Warping (DTW) algorithm that searches for the minimum distance by changing and comparing time-points, rather than using the Euclidean algorithm, which compares the same time-point. Both Euclidean and DTW algorithms were applied to nine water quality variables to identify similarities between stations, and K-medoids cluster analysis were performed based on the similarity. The Clustering Validation Index (CVI) was used to select the optimal number of clusters. Our results show that the Euclidean algorithm formed clusters by mixing mainstream and tributary stations; the mainstream stations were largely divided into three different clusters. In contrast, the DTW algorithm formed clear clusters by reflecting the characteristics of water quality and watershed. Furthermore, because the Euclidean algorithm requires the lengths of the time series to be the same, data loss was inevitable. As a result, even where clusters were the same as those obtained by DTW, the characteristics of the water quality variables in the cluster differed. The DTW analysis in this study provides useful information for understanding the similarity or difference in water parameter values between different locations. Thus, the number and location of required monitoring stations can be adjusted to improve the efficiency of field monitoring network management.
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Keywords:
dynamic time warping; water quality network optimization; cluster analysis; river water system; water quality characteristics
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MDPI and ACS Style
Lee, S.; Kim, J.; Hwang, J.; Lee, E.; Lee, K.-J.; Oh, J.; Park, J.; Heo, T.-Y. Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network. Water 2020, 12, 2411. https://doi.org/10.3390/w12092411
AMA Style
Lee S, Kim J, Hwang J, Lee E, Lee K-J, Oh J, Park J, Heo T-Y. Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network. Water. 2020; 12(9):2411. https://doi.org/10.3390/w12092411
Chicago/Turabian StyleLee, Seulbi, Jaehoon Kim, Jongyeon Hwang, EunJi Lee, Kyoung-Jin Lee, Jeongkyu Oh, Jungsu Park, and Tae-Young Heo. 2020. "Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network" Water 12, no. 9: 2411. https://doi.org/10.3390/w12092411
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