A novel SON2 -based similarity index and its application for the rationalization of river water quality monitoring network

2017 ◽  
Vol 34 (2) ◽  
pp. 144-152 ◽  
Author(s):  
D. Antanasijević ◽  
V. Pocajt ◽  
J. Antanasijević ◽  
A. Perić-Grujić ◽  
M. Ristić
Keyword(s):  
Water Quality ◽  
River Water ◽  
Similarity Index ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
River Water Quality ◽  
Quality Monitoring ◽  
Water Quality Monitoring Network

The role of institutional and legal constraints on river water quality monitoring in Ukraine

2014 ◽  
Vol 72 (12) ◽  
pp. 4745-4756 ◽  
Author(s):  
Nina Hagemann ◽  
Bernd Klauer ◽  
Ruby M. Moynihan ◽  
Marco Leidel ◽  
Nicole Scheifhacken
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Monitoring ◽  
River Water Quality ◽  
Quality Monitoring ◽  
Legal Constraints

scholarly journals Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2411
Author(s):  
Seulbi Lee ◽  
Jaehoon Kim ◽  
Jongyeon Hwang ◽  
EunJi Lee ◽  
Kyoung-Jin Lee ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Euclidean Algorithm ◽  
Quality Monitoring ◽  
Quality Data ◽  
Time Warping ◽  
Water Quality Data ◽  
Dynamic Time

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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