IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i9d10.1007_s11269-018-1971-z.html
   My bibliography  Save this article

Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning

Author

Listed:
  • Ata Allah Nadiri

    (University of Tabriz)

  • Maryam Gharekhani

    (University of Tabriz)

  • Rahman Khatibi

    (GTEV-ReX Limited)

Abstract

DRASTIC-based vulnerability indices and their variations for an aquifer are investigated in this paper, each of which is regarded as a framework since their rationale of using seven DRASTIC data layers is consensual and lacks empirical or theoretical formulations. The Basic DRASTIC framework (BDF) is implemented by a set of prescribed rules; whereas its three variations involve unsupervised learning from the data, which comprise: (i) learning the rates by the Wilcoxon test (WDF) but using BDF weights; (ii) using BDF rates but learning the weights by Genetic Algorithm (BDF-GA); and (iii) learning rates as in WDF and the weights as in BDF-GA (WDF-GA). These four frameworks are not supervised, but the novelty of the paper is to introduce supervised learning at the second stage by Artificial Intelligence to run Multiple Frameworks (AIMF), for which the paper uses Support Vector Machine (SVM). AIMF uses the outputs of the four frameworks as its input data and a function of observed nitrate-N values as its target data. The AIMF strategy is evaluated in the aquifer of Ardabil plain, which is exposed to anthropogenic contamination such as nitrate-N. The coefficient of correlation (r-values) between the results and nitrate-N values for the above frameworks are: 0.2, 0.37, 0.38 and 0.45; whereas AIMF enhances it to 0.84; attributable to the supervised learning.

Suggested Citation

  • Ata Allah Nadiri & Maryam Gharekhani & Rahman Khatibi, 2018. "Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3023-3040, July.
  • Handle: RePEc:spr:waterr:v:32:y:2018:i:9:d:10.1007_s11269-018-1971-z
    DOI: 10.1007/s11269-018-1971-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-1971-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-018-1971-z?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. Wenyong Wu & Shiyang Yin & Honglu Liu & Honghan Chen, 2014. "Groundwater Vulnerability Assessment and Feasibility Mapping Under Reclaimed Water Irrigation by a Modified DRASTIC Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1219-1234, March.
    2. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    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. Mahmoud Mohammad Rezapour Tabari & Mohsen Mazak Mari, 2016. "The Integrated Approach of Simulation and Optimization in Determining the Optimum Dimensions of Canal for Seepage Control," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1271-1292, February.
    2. Krishnakumar Subramanian & V. Sreevidya & R. Venkatasubramani & Vivek Sivakumar, 2023. "DRASTIC model developed with lineament density to map groundwater susceptibility: a case study in part of Coimbatore district, Tamilnadu, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10411-10423, September.
    3. Gokmen Tayfur & Luca Brocca, 2015. "Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3519-3533, August.
    4. Longxia Qian & Ren Zhang & Mei Hong & Hongrui Wang & Lizhi Yang, 2016. "A new multiple integral model for water shortage risk assessment and its application in Beijing, China," 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. 80(1), pages 43-67, January.
    5. Sina Sadeghfam & Yousef Hassanzadeh & Ata Allah Nadiri & Mahdi Zarghami, 2016. "Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4585-4601, October.
    6. M. A. Ghorbani & R. Khatibi & V. Karimi & Zaher Mundher Yaseen & M. Zounemat-Kermani, 2018. "Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4201-4215, October.
    7. Xinyue Ke & Ni Wang & Long Yu & Zihan Guo & Tianming He, 2023. "Spatial Distribution of Water Risk Based on Atlas Compilation in the Shaanxi Section of the Qinling Mountains, China," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

    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:spr:waterr:v:32:y:2018:i:9:d:10.1007_s11269-018-1971-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.