An automatic graph-based method for characterizing multichannel networks
An automatic graph-based method for characterizing multichannel networks
Assessment and quantitative description of river morphology using widely recognized river planview measures (e.g., length, width and sinuosity of channels, bifurcation angles and island shape) for multichannel rivers are regarded as fundamental parts of the toolkit of geomorphologists and river engineers. However, conventional assessment methods including field surveys or exiting algorithms for the extraction of multichannel planviews might be suboptimal. More recently, the potential for the application of complex network analysis to the study of river morphology has led to emphasis on the accurate characterization and definition of multichannel network topology. Therefore, we developed a novel algorithm called RivMACNet (River Morphological Analysis based on Complex Networks) that enables the extraction of multichannel network topology using satellite sensor images as the input. We applied RivMACNet to a meandering reach of the Yangtze River and a strongly anastomosing reach of the Indus River to construct their network topologies, and then calculated a series of common topological measures including weighted degree (WD), clustering coefficient (CC) and weighted characteristic path length (WCPL). The network analysis indicated that both networks exhibit poor transitivity with small clustering coefficients. The topological properties of the Indus at the reach scale are independent of flow conditions, while they vary across space at the subnetwork scale. In addition, comparison between RivMACNet and an alternative common river network analysis engine (RivaMap) demonstrated that RivMACNet is superior in terms of representation accuracy and network connectivity and, thus, is more suitable for multichannel fluvial systems with complex planviews. RivMACNet is, thus, a useful tool to support further investigation of multichannel river networks using graph theory.
Complex network analysis, Graph theory, Multichannel network, Remote sensing, River network topology
Liu, Yanhui
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Carling, Paul A.
8d252dd9-3c88-4803-81cc-c2ec4c6fa687
Wang, Yuanjian
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Jiang, Enhui
0cabe3ea-d7ea-4479-b617-5395d1f14730
Atkinson, Peter M.
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September 2022
Liu, Yanhui
cb90c206-b9f5-4202-8cdc-1b8c9cb7d8bc
Carling, Paul A.
8d252dd9-3c88-4803-81cc-c2ec4c6fa687
Wang, Yuanjian
f87b7874-9ca7-422d-b131-b688cee2eca1
Jiang, Enhui
0cabe3ea-d7ea-4479-b617-5395d1f14730
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Liu, Yanhui, Carling, Paul A., Wang, Yuanjian, Jiang, Enhui and Atkinson, Peter M.
(2022)
An automatic graph-based method for characterizing multichannel networks.
Computers & Geosciences, 166, [105180].
(doi:10.1016/j.cageo.2022.105180).
Abstract
Assessment and quantitative description of river morphology using widely recognized river planview measures (e.g., length, width and sinuosity of channels, bifurcation angles and island shape) for multichannel rivers are regarded as fundamental parts of the toolkit of geomorphologists and river engineers. However, conventional assessment methods including field surveys or exiting algorithms for the extraction of multichannel planviews might be suboptimal. More recently, the potential for the application of complex network analysis to the study of river morphology has led to emphasis on the accurate characterization and definition of multichannel network topology. Therefore, we developed a novel algorithm called RivMACNet (River Morphological Analysis based on Complex Networks) that enables the extraction of multichannel network topology using satellite sensor images as the input. We applied RivMACNet to a meandering reach of the Yangtze River and a strongly anastomosing reach of the Indus River to construct their network topologies, and then calculated a series of common topological measures including weighted degree (WD), clustering coefficient (CC) and weighted characteristic path length (WCPL). The network analysis indicated that both networks exhibit poor transitivity with small clustering coefficients. The topological properties of the Indus at the reach scale are independent of flow conditions, while they vary across space at the subnetwork scale. In addition, comparison between RivMACNet and an alternative common river network analysis engine (RivaMap) demonstrated that RivMACNet is superior in terms of representation accuracy and network connectivity and, thus, is more suitable for multichannel fluvial systems with complex planviews. RivMACNet is, thus, a useful tool to support further investigation of multichannel river networks using graph theory.
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Accepted/In Press date: 17 June 2022
e-pub ahead of print date: 19 June 2022
Published date: September 2022
Additional Information:
Funding Information:
This research was funded and supported partially by the National Key Research and Development Program of China (No. 2021YFC3200400 ), the National Natural Science Foundation of China ( NSFC , No. 42041004 ), the Provincial Science Fund for Excellent Young Scholars of Henan (No. 202300410540 ), the China Scholarship Council (No. CSC201906710092 ) and Hohai University, Nanjing . The research was undertaken while Liu Yanhui visited Lancaster University in 2020.
Publisher Copyright:
© 2022
Keywords:
Complex network analysis, Graph theory, Multichannel network, Remote sensing, River network topology
Identifiers
Local EPrints ID: 468025
URI: http://eprints.soton.ac.uk/id/eprint/468025
ISSN: 0098-3004
PURE UUID: 530ff38d-3c2c-4b4a-9162-68dd0aef797d
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Date deposited: 28 Jul 2022 16:32
Last modified: 17 Mar 2024 02:40
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Contributors
Author:
Yanhui Liu
Author:
Yuanjian Wang
Author:
Enhui Jiang
Author:
Peter M. Atkinson
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