IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v597y2022ics0378437122002084.html
   My bibliography  Save this article

Analysis of precipitation characteristics in Shanghai based on the visibility graph algorithm

Author

Listed:
  • Cao, Run-Hua
  • Deng, Zheng-Hong
  • Xu, Ji-Wei

Abstract

Most of the existing studies on the characteristics of precipitation in Shanghai only focus on the statistical characteristics of the precipitation series rather than the internal dynamic characteristic. The visibility graph algorithm has been proven to be an effective algorithm for grasping the internal dynamics characteristics of time series. Therefore, this paper exploits the visibility graph algorithm to analyze the daily precipitation time series in Shanghai from 2000 to 2020. Some characteristics of precipitation during this period are obtained: Typhoon Mesa and Typhoon Fitow had a profound impact on Shanghai’s precipitation; There is a certain similarity of the precipitation structure in different periods; Only a few nodes in the precipitation network have a profound impact on the precipitation in Shanghai.

Suggested Citation

  • Cao, Run-Hua & Deng, Zheng-Hong & Xu, Ji-Wei, 2022. "Analysis of precipitation characteristics in Shanghai based on the visibility graph algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    • Handle: RePEc:eee:phsmap:v:597:y:2022:i:c:s0378437122002084
    DOI: 10.1016/j.physa.2022.127227
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122002084
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127227?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Keshi & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2020. "Visibility graph analysis of Bitcoin price series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    2. Yang, Yue & Yang, Huijie, 2008. "Complex network-based time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1381-1386.
    3. Wang, Na & Li, Dong & Wang, Qiwen, 2012. "Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6543-6555.
    4. Wei Zhang & Gabriele Villarini & Gabriel A. Vecchi & James A. Smith, 2018. "Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston," Nature, Nature, vol. 563(7731), pages 384-388, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sulaimany, Sadegh & Mafakheri, Aso, 2023. "Visibility graph analysis of web server log files," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Jinjun & Wang, Yinhai & Liu, Fang, 2013. "Characterizing traffic time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4192-4201.
    2. Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2019. "Visibility graph analysis of economy policy uncertainty indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    3. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    4. Liu, Hao-Ran & Li, Ming-Xia & Zhou, Wei-Xing, 2024. "Visibility graph analysis of the grains and oilseeds indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    5. Tang, Jinjun & Wang, Yinhai & Wang, Hua & Zhang, Shen & Liu, Fang, 2014. "Dynamic analysis of traffic time series at different temporal scales: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 303-315.
    6. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    7. Joowon Im, 2019. "Green Streets to Serve Urban Sustainability: Benefits and Typology," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    8. Sudhamayee, K. & Krishna, M. Gopal & Manimaran, P., 2023. "Simplicial network analysis on EEG signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    9. Jiaxing Cui & Xuesong Kong & Jing Chen & Jianwei Sun & Yuanyuan Zhu, 2021. "Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt," Land, MDPI, vol. 10(1), pages 1-24, January.
    10. Susca, T. & Zanghirella, F. & Colasuonno, L. & Del Fatto, V., 2022. "Effect of green wall installation on urban heat island and building energy use: A climate-informed systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    11. Liu, Keshi & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2020. "Visibility graph analysis of Bitcoin price series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    12. Schmidt, Jonas & Köhne, Daniel, 2023. "A simple scalable linear time algorithm for horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    13. Yuan, Qianshun & Zhang, Jing & Wang, Haiying & Gu, Changgui & Yang, Huijie, 2023. "A multi-scale transition matrix approach to chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    14. Gainbi Park & Zengwang Xu, 2022. "The constituent components and local indicator variables of social vulnerability index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 95-120, January.
    15. Yan-Hong Yang & Ying-Lin Liu & Ying-Hui Shao, 2023. "Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," Papers 2310.18903, arXiv.org, revised Jun 2024.
    16. Carlynn Fagnant & Avantika Gori & Antonia Sebastian & Philip B. Bedient & Katherine B. Ensor, 2020. "Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1597-1621, November.
    17. Weijiang Li & Jiahong Wen & Bo Xu & Xiande Li & Shiqiang Du, 2018. "Integrated Assessment of Economic Losses in Manufacturing Industry in Shanghai Metropolitan Area Under an Extreme Storm Flood Scenario," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    18. Tim Slack & Vanessa Parks & Lynsay Ayer & Andrew M. Parker & Melissa L. Finucane & Rajeev Ramchand, 2020. "Natech or natural? An analysis of hazard perceptions, institutional trust, and future storm worry following Hurricane Harvey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1207-1224, July.
    19. Xue Pan & Lei Hou & Mutua Stephen & Huijie Yang & Chenping Zhu, 2014. "Evaluation of Scaling Invariance Embedded in Short Time Series," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-27, December.
    20. Vinícius B. P. Chagas & Pedro L. B. Chaffe & Günter Blöschl, 2022. "Climate and land management accelerate the Brazilian water cycle," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:597:y:2022:i:c:s0378437122002084. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.