IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0298664.html
   My bibliography  Save this article

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Author

Listed:
  • Limei Dong
  • Xinyu Zuo
  • Yiping Xiong

Abstract

Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

Suggested Citation

  • Limei Dong & Xinyu Zuo & Yiping Xiong, 2024. "Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0298664
    DOI: 10.1371/journal.pone.0298664
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298664
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298664&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0298664?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
    ---><---

    References listed on IDEAS

    as
    1. Xiangfeng Bu & Kai Liu & Jingyu Liu & Yunhong Ding, 2023. "A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data," Sustainability, MDPI, vol. 15(21), pages 1-26, October.
    2. Ivana Krtolica & Dragan Savić & Bojana Bajić & Snežana Radulović, 2022. "Machine Learning for Water Quality Assessment Based on Macrophyte Presence," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    3. Sarah A Tominack & Kara Z Coffey & David Yoskowitz & Gail Sutton & Michael S Wetz, 2020. "An assessment of trends in the frequency and duration of Karenia brevis red tide blooms on the South Texas coast (western Gulf of Mexico)," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    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.

      More about this item

      Statistics

      Access and download statistics

      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:plo:pone00:0298664. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      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.