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A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

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  • Seyed Naghibi
  • Hamid Pourghasemi

Abstract

As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. Therefore, the objective of current study is to evaluate the capability of three machine learning models such as boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF), and comparison of their performance by bivariate (evidential belief function (EBF)), and multivariate (general linear model (GLM)) statistical methods in the groundwater potential mapping. This study was carried out in the Beheshtabad Watershed, Chaharmahal-e-Bakhtiari Province, Iran. In total, 1425 spring locations were detected in the study area. Seventy percent of the spring locations were used for model training, and 30 % for validation purposes. Fourteen conditioning-factors were considered in this investigation, including slope angle, slope aspect, altitude, plan curvature, profile curvature, slope length (LS), stream power index (SPI), topographic wetness index (TWI), distance from rivers, distance from faults, river density, fault density, lithology, and land use. Using the above conditioning factors and different algorithms, groundwater potential maps were generated, and the results were plotted in ArcGIS 9.3. According to the results of success rate curves (SRC), values of area under the curve (AUC) for the five models vary from 0.692 to 0.975. In contrast, the AUC for prediction rate curves (PRC) ranges from 77.26 to 86.39 %. The CART, BRT, and RF machine learning techniques showed very good performance in groundwater potential mapping with the AUC values of 86.39, 86.12, and 86.05 %, respectively. By the way, The GLM and EBF models in comparison by machine learning models showed weaker performance in spring groundwater potential mapping by the AUC values of 77.26, and 67.72 %, respectively. The proposed methods provided rapid, accurate, and cost effective results. Furthermore, the analysis may be transferable to other watersheds with similar topographic and hydro-geological characteristics. Copyright Springer Science+Business Media Dordrecht 2015

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  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:14:p:5217-5236
    DOI: 10.1007/s11269-015-1114-8
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    1. Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
    2. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    3. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    4. R. Mazza & F. La Vigna & C. Alimonti, 2014. "Evaluating the Available Regional Groundwater Resources Using the Distributed Hydrogeological Budget," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 749-765, February.
    5. Mostafa Moradi Dashtpagerdi & Ahmad Nohegar & Hassan Vagharfard & Afshin Honarbakhsh & Vafa Mahmoodinejad & Akbar Noroozi & Diba Ghonchehpoor, 2013. "Application of Spatial Analysis Techniques to Select the Most Suitable Areas for Flood Spreading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3071-3084, June.
    6. Slim Zekri & Chefi Triki & Ali Al-Maktoumi & Mohammad Bazargan-Lari, 2015. "An Optimization-Simulation Approach for Groundwater Abstraction under Recharge Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3681-3695, August.
    7. Juan Esquivel & Guillermo Morales & María Esteller, 2015. "Groundwater Monitoring Network Design Using GIS and Multicriteria Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3175-3194, July.
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    4. Ahmed Madani & Burhan Niyazi, 2023. "Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia," Sustainability, MDPI, vol. 15(3), pages 1-15, February.
    5. Yong Ye & Wei Chen & Guirong Wang & Weifeng Xue, 2022. "Spatial Prediction of the Groundwater Potential Using Remote Sensing Data and Bivariate Statistical-Based Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5461-5494, November.
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    7. Soyoung Park & Se-Yeong Hamm & Hang-Tak Jeon & Jinsoo Kim, 2017. "Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
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    11. 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.

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