1. Stream gauge network grouping analysis using community detection
- Author
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Myungjin Lee, Jaewon Kwak, Jaewon Jung, Jongsung Kim, Hongjun Joo, and Hung Soo Kim
- Subjects
Environmental Engineering ,010504 meteorology & atmospheric sciences ,Computer science ,media_common.quotation_subject ,0208 environmental biotechnology ,Drainage basin ,Cohesion (computer science) ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Stream gauge ,Adaptability ,Environmental Chemistry ,Safety, Risk, Reliability and Quality ,0105 earth and related environmental sciences ,General Environmental Science ,Water Science and Technology ,media_common ,Modularity (networks) ,geography ,geography.geographical_feature_category ,Complex network ,020801 environmental engineering ,Stage (hydrology) ,Data mining ,Scale (map) ,computer - Abstract
Stream gauging stations are important in hydrology and water science for obtaining water-related information, such as stage and discharge. However, for efficient operation and management, a more accurate grouping method is needed, which should be based on the interrelationships between stream gauging stations. This study presents a grouping method that employs community detection based on complex networks. The proposed grouping method was compared with the cluster analysis approach, which is based on statistics, to verify its adaptability. To achieve this goal, 39 stream gauging stations in the Yeongsan River basin of South Korea were investigated. The numbers of groups (clusters) in the study were two, four, six, and eight, which were determined to be suitable by fusion coefficient analysis. Ward’s method was employed for cluster analysis, and multilevel modularity optimization was applied for community detection. A higher level of cohesion between stream gauging stations was observed in the community detection method at the basin scale and the stream link scale within the basin than in the cluster analysis. This suggests that community detection is more effective than cluster analysis in terms of hydrologic similarity, persistence, and connectivity. As such, these findings could be applied to grouping methods for efficient operation and maintenance of stream gauging stations.
- Published
- 2020