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Strategies for Learning Groundwater Potential Modelling Indices under Sparse Data with Supervised and Unsupervised Techniques

Author

Listed:
  • V. Karimi

    (University of Tabriz)

  • R. Khatibi

    (GTEV-ReX Limited)

  • M. A. Ghorbani

    (University of Tabriz
    Near East University
    Istanbul Technical University)

  • D. Tien Bui

    (University of South-Eastern Norway (USN))

  • S. Darbandi

    (University of Tabriz)

Abstract

Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data by the customary ten general-purpose data layers with a scoring system of rates and weights but assigning their values give rise to subjectivity. Learning rates/weights from site-specific data reduces subjectivity through unsupervised models. The use of supervised models requires target values, and the paper derives their values from the record at all the productive wells by developing a binary classification model. The paper formulates an Inclusive Multiple Modelling (IMM) strategy to learn from the site data at two levels: at Level 1: two unsupervised ‘base’ models and four supervised ‘base’ models are investigated; at Level 2 the IMM strategies include a supervised ‘combiner’ model, which uses outputs of unsupervised base models; as well as an unsupervised ‘combiner’ model, which uses outputs of supervised base models. Performance metrics are derived by the Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC). The results show that unsupervised learning at Level 2 (using supervised base models) may reduce subjectivity but even supervised learning at Level 1 can be effective in extracting essential information from target values. Although unsupervised models would extract marginal information from models at Level 1, a supervised model at Level 2 can extract good information from unsupervised models at Level 1. Graphical Abstract

Suggested Citation

  • V. Karimi & R. Khatibi & M. A. Ghorbani & D. Tien Bui & S. Darbandi, 2020. "Strategies for Learning Groundwater Potential Modelling Indices under Sparse Data with Supervised and Unsupervised Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2389-2417, June.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:8:d:10.1007_s11269-020-02555-y
    DOI: 10.1007/s11269-020-02555-y
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    3. J. Grande & Jose Andújar & Javier Aroba & Rafael Beltrán & Maria de la Torre & Juan Cerón & T. Gómez, 2010. "Fuzzy Modeling of the Spatial Evolution of the Chemistry in the Tinto River (SW Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(12), pages 3219-3235, September.
    4. Deepesh Machiwal & Madan Jha & Bimal Mal, 2011. "Assessment of Groundwater Potential in a Semi-Arid Region of India Using Remote Sensing, GIS and MCDM Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1359-1386, March.
    5. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
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