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Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach

Author

Listed:
  • Shaghayegh Miraki

    (University of Agricultural Science and Natural Resources of Sari)

  • Sasan Hedayati Zanganeh

    (University of Tabriz)

  • Kamran Chapi

    (University of Kurdistan)

  • Vijay P. Singh

    (Texas A & M University)

  • Ataollah Shirzadi

    (University of Kurdistan)

  • Himan Shahabi

    (University of Kurdistan)

  • Binh Thai Pham

    (University of Transport Technology)

Abstract

Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier ensemble method, namely Random Forest Classifier based on Random Subspace Ensemble (RS-RF), for groundwater potential mapping (GWPM) in Qorveh-Dehgolan plain, Kurdistan province, Iran. A total of 12 conditioning factors (slope, aspect, elevation, curvature, stream power index (SPI), topographic wetness index (TWI), rainfall, lithology, land use, normalized difference vegetation index (NDVI), fault density, and river density) were selected for groundwater modeling. The least square support vector machine (LSSVM) feature selection method with a 10-fold cross-validation technique was used to validate the predictive capability of these conditioning factors for training the models. The performance of the RS-RF model was validated using the area under receiver operating characteristic curve (AUROC), success and prediction rate curves, kappa index, and several statistical index-based measures. In addition, Friedman and Wilcoxon signed-rank tests were used to assess statistically significant level among the new model with the state-of-the-art soft computing benchmark models, such as random forest (RF), logistic regression (LR) and naïve Bayes (NB). Results showed that the new hybrid model of RS-RF had a very high predictive capability for groundwater potential mapping and exhibited the best performance among other benchmark models (LR, RF, and NB). Results of the present study might be useful to water managers to make proper decisions on the optimal use of groundwater resources for future planning in the critical study area.

Suggested Citation

  • Shaghayegh Miraki & Sasan Hedayati Zanganeh & Kamran Chapi & Vijay P. Singh & Ataollah Shirzadi & Himan Shahabi & Binh Thai Pham, 2019. "Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 281-302, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:1:d:10.1007_s11269-018-2102-6
    DOI: 10.1007/s11269-018-2102-6
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    References listed on IDEAS

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    1. Madan Jha & Alivia Chowdhury & V. Chowdary & Stefan Peiffer, 2007. "Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 427-467, February.
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    7. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
    8. Emna Boughariou & Nabila Allouche & Fatma Ben Brahim & Ghada Nasri & Salem Bouri, 2021. "Delineation of groundwater potentials of Sfax region, Tunisia, using fuzzy analytical hierarchy process, frequency ratio, and weights of evidence models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 14749-14774, October.
    9. Amirhosein Mosavi & Farzaneh Sajedi Hosseini & Bahram Choubin & Massoud Goodarzi & Adrienn A. Dineva & Elham Rafiei Sardooi, 2021. "Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 23-37, January.
    10. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    11. Dieu Tien Bui & Ataollah Shirzadi & Ata Amini & Himan Shahabi & Nadhir Al-Ansari & Shahriar Hamidi & Sushant K. Singh & Binh Thai Pham & Baharin Bin Ahmad & Pezhman Taherei Ghazvinei, 2020. "A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
    12. Weiyu Yu & Nicola A Wardrop & Robert E S Bain & Victor Alegana & Laura J Graham & Jim A Wright, 2019. "Mapping access to domestic water supplies from incomplete data in developing countries: An illustrative assessment for Kenya," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
    13. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).

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