Optimal Stream Gauge Network Design Using Entropy Theory and Importance of Stream Gauge Stations

被引:5
|
作者
Joo, Hongjun [1 ]
Lee, Jiho [2 ]
Jun, Hwandon [2 ]
Kim, Kyungtak [3 ]
Hong, Seungjin [3 ]
Kim, Jungwook [1 ]
Kim, Hung Soo [1 ]
机构
[1] Inha Univ, Dept Civil Engn, Incheon 22212, South Korea
[2] Seoul Natl Univ Sci & Technol, Dept Civil Engn, Seoul 01811, South Korea
[3] Korea Inst Civil Engn & Bldg Technol, Dept Hydro Sci & Engn Res, Ilsan 10223, South Korea
关键词
stream gauge network; entropy; station rating; Euclidean distance; MONITORING NETWORK; OPTIMIZATION; RAINFALL;
D O I
10.3390/e21100991
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Stream gauge stations are facilities for measuring stream water levels and flow rates, and their main purpose is to produce the data required to analyze hydrological phenomena. However, there are no specific criteria for selecting the locations and installation densities of stream gauge stations, which results in numerous problems, including regional imbalances and overlapping. To address these issues, a stream gauge network was constructed in this study considering both the transinformation of entropy (objective function 1) and the importance of each stream gauge station (objective function 2). To account for both factors, the optimal combinations that satisfied the two objective functions were determined using the Euclidean distance. Based on the rainfall runoff analysis results, unit hydrographs reflecting stream connectivity were derived and applied to entropy theory. The importance of each stream gauge station was calculated considering its purposes, such as flood control, water use, and environment. When this method was applied to the Namgang Dam Basin, it was found out that eight out of 12 stream gauge stations were required. The combination of the selected stations reflected both the transinformation of entropy and the importance of each station.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density
    Tiwari, Shubham
    Jha, Sanjeev Kumar
    Singh, Ankit
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [32] Design of a pulse stream neural network
    Haycock, RJ
    York, TA
    40TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1 AND 2, 1998, : 1053 - 1056
  • [33] Gauge theory for the rate equations: Electrodynamics on a network
    Timm, Carsten
    PHYSICAL REVIEW LETTERS, 2007, 98 (07)
  • [34] Rain gauge network design using coupled geostatistical and multivariate techniques
    Shaghaghian, M. R.
    Abedini, M. J.
    SCIENTIA IRANICA, 2013, 20 (02) : 259 - 269
  • [35] An algorithm for optimisation of a rain gauge network based on geostatistics and entropy concepts using GIS
    Mahmoudi-Meimand, Hadi
    Nazif, Sara
    Abbaspour, Rahim Ali
    Sabokbar, Hasanali Faraji
    JOURNAL OF SPATIAL SCIENCE, 2016, 61 (01) : 233 - 252
  • [36] Entropy in Poincare gauge theory: Kerr-AdS solution
    Blagojevic, M.
    Cvetkovic, B.
    PHYSICAL REVIEW D, 2020, 102 (06):
  • [37] Extremal Kerr black hole entropy in Poincare´ gauge theory
    Cvetkovic, B.
    Rakonjac, D.
    PHYSICAL REVIEW D, 2023, 107 (04)
  • [38] Entanglement entropy in scalar field theory and ZM gauge theory on Feynman diagrams
    Iso, Satoshi
    Mori, Takato
    Sakai, Katsuta
    PHYSICAL REVIEW D, 2021, 103 (10)
  • [39] Optimal design of a micromachined force gauge under uncertainty
    Mawardi, A
    Pitchumani, R
    JOURNAL OF MICROMECHANICS AND MICROENGINEERING, 2005, 15 (12) : 2353 - 2365
  • [40] Optimal operation of heat exchanger network with stream splitting
    Mohanan, Karthika
    Jogwar, Sujit
    IFAC PAPERSONLINE, 2022, 55 (34): : 78 - 83