Stream gauge network grouping analysis using community detection

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.

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

“…Huang et al [22] carried out a survey on techniques of community detection in multilayer networks. Rostami et al [23] presented a genetic algorithm for feature selection that is based on a novel community detection, Li et al [24] proposed the convex relaxation techniques for community detection, and Joo et al [25] utilized the community detection for studying the stream gauge network grouping. e modularity is employed to reflect the fraction of edges using the communities related to the amount of edges developed using communities.…”

Impact Analysis of Weather Factors on Aircraft Cancellation using Multilayer Complex Network

“…The complex network method converts an object into a network and then analyzes to identify characteristics of the target and its factors, the relationships between the components and so on. It can express a complicated event as a simple graph (or network) and is used in various elds, due to its high applicability (Joo et al 2021). The rst step in applying a complex network analysis is to de ne "nodes" and "links", which are the basic factors of a network.…”

“…Various researchers have applied the method to various data in different elds and developed it into a new eld called "network science". The complex network can simplify complex phenomena visually into a graph (or a network) and derive useful information such as features of the target and an understanding of the physical behaviors, roles and interactions of components and their relationships (Joo et al 2021). Aircraft studies have considered this in elucidating the characteristics of airline networks, airports and air routes (Bagler 2008 Zheng, 2020).…”

Selection of representative indicators for flood risk assessment using marginal entropy and mutual information

“…Indicators with high correlation values (indicators with overlapping meanings) can be considered as targets for removal by analyzing the tendencies of indicators (variables). Pearson correlation analysis has also been frequently applied in previous studies, mainly to understand the rules of complex hydrological phenomena (Joo, Kim et al, 2021; Joo, Lee et al, 2021; Sivakumar, 2007; Sivakumar & Singh, 2012; Sivakumar & Woldemeskel, 2014; Yasmin & Sivakumar, 2018). However, as this method analyzes linear relationships between variables, it is not reliable when applied to nonlinear data, such as flood risk indicators.…”