IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13852-d1242145.html
   My bibliography  Save this article

Water-Richness Zoning Technology of Karst Aquifers at in the Roofs of Deep Phosphate Mines Based on Random Forest Model

Author

Listed:
  • Xin Li

    (College of Resource and Environmental Engineering, Guizhou University, Guiyang 550025, China)

  • Bo Li

    (College of Resource and Environmental Engineering, Guizhou University, Guiyang 550025, China
    Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang 550025, China)

  • Ye Luo

    (Guizhou Kailin Phophate Industry Co., Ltd., Guiyang 550025, China)

  • Tao Li

    (School of Mines and Civil Engineering, Liupanshui Normal University, Liupanshui 553004, China)

  • Hang Han

    (Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang 550025, China)

  • Wenjie Zhang

    (College of Resource and Environmental Engineering, Guizhou University, Guiyang 550025, China)

  • Beibei Zhang

    (College of Building Science and Engineering, Guiyang University, Guiyang 550025, China)

Abstract

The development of fractures and conduits in karst aquifers and the strength of their water richness are key factors in determining whether a water intrusion will occur in a mine. In the phosphorus mining process, if the mining of water-rich areas is carried out, sudden water disasters can easily occur. Therefore, water-richness zoning of the karst aquifer on the roof of the phosphate mine is very important to protect against the incidence of water disasters in the mine. This paper proposes a random-forest-based partitioning model of the water richness of phosphate mine roofs in karst areas based on the random forest intelligence algorithm in machine learning. Taking a productive phosphate mine in southern China as a typical case, seven main assessment indicators affecting the water richness of the phosphate mine roof aquifer were determined. The proposed random forest model was utilized to determine the weight of each evaluation index, and the water richness of the karst aquifer on the roof of this phosphate mine was studied by zoning. The whole structure of the mine is highly water-rich, with strongly water-rich areas mainly concentrated in the central and northeastern part of the mine. The water-richness fitting rates (WFP) introduced for validation were all in agreement with the evaluation results, and the constructed model met the accuracy requirements. The study’s findings can serve as a guide for mine design and water-disaster warnings in karst regions.

Suggested Citation

  • Xin Li & Bo Li & Ye Luo & Tao Li & Hang Han & Wenjie Zhang & Beibei Zhang, 2023. "Water-Richness Zoning Technology of Karst Aquifers at in the Roofs of Deep Phosphate Mines Based on Random Forest Model," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13852-:d:1242145
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13852/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/18/13852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Noah J. Planavsky & Olivier J. Rouxel & Andrey Bekker & Stefan V. Lalonde & Kurt O. Konhauser & Christopher T. Reinhard & Timothy W. Lyons, 2010. "The evolution of the marine phosphate reservoir," Nature, Nature, vol. 467(7319), pages 1088-1090, October.
    2. Daolei Xie & Jing Han & Huide Zhang & Kai Wang & Zhongwen Du & Tianyu Miao, 2022. "Risk Assessment of Water Inrush from Coal Seam Roof Based on Combination Weighting-Set Pair Analysis," Sustainability, MDPI, vol. 14(19), 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.
    1. Tao Yan & Chuanqu Zhu & Qingfeng Li & Qian Xu, 2023. "Investigating Disaster Mechanisms Triggered by Abrupt Overburden Fracture Alterations in Close-Seam Mining Beneath an Exceptionally Thick Sandstone Aquifer," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    2. Lele Xiao & Fan Li & Chao Niu & Gelian Dai & Qian Qiao & Chengsen Lin, 2022. "Evaluation of Water Inrush Hazard in Coal Seam Roof Based on the AHP-CRITIC Composite Weighted Method," Energies, MDPI, vol. 16(1), pages 1-20, December.
    3. Ernest Chi Fru & Jalila Al Bahri & Christophe Brosson & Olabode Bankole & Jérémie Aubineau & Abderrazzak El Albani & Alexandra Nederbragt & Anthony Oldroyd & Alasdair Skelton & Linda Lowhagen & David , 2023. "Transient fertilization of a post-Sturtian Snowball ocean margin with dissolved phosphate by clay minerals," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Wang Zheng & Anwen Zhou & Swapan K. Sahoo & Morrison R. Nolan & Chadlin M. Ostrander & Ruoyu Sun & Ariel D. Anbar & Shuhai Xiao & Jiubin Chen, 2023. "Recurrent photic zone euxinia limited ocean oxygenation and animal evolution during the Ediacaran," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Jinzhou Peng & Dengfeng Li & Simon W. Poulton & Gary J. O’Sullivan & David Chew & Yu Fu & Xiaoming Sun, 2024. "Episodic intensification of marine phosphorus burial over the last 80 million years," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Baoxin Zhao & Qimeng Liu & Jingzhong Zhu, 2023. "Risk Assessment and Zonation of Roof Water Inrush Based on the Analytic Hierarchy Process, Principle Component Analysis, and Improved Game Theory (AHP–PCA–IGT) Method," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    7. Lennart Ramme & Tatiana Ilyina & Jochem Marotzke, 2024. "Moderate greenhouse climate and rapid carbonate formation after Marinoan snowball Earth," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Aorui Bi & Shuya Huang & Xinguo Sun, 2023. "Risk Assessment of Oil and Gas Pipeline Based on Vague Set-Weighted Set Pair Analysis Method," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    9. Daolei Xie & Zhongwen Du & Chenghao Han & Jie Han & Jiuchuan Wei & Jiulei Yan, 2023. "Prediction of the Water Inrush Risk from an Overlying Separation Layer in the Thick Overburden of a Thick Coal Seam," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    10. Jie Liu & Qian Ma & Wanqing Wang & Guanding Yang & Haowen Zhou & Xinyue Hu & Liangyun Teng & Xuehua Luo, 2022. "Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents," Sustainability, MDPI, vol. 14(24), pages 1-22, 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:gam:jsusta:v:15:y:2023:i:18:p:13852-:d:1242145. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.