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Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

Author

Listed:
  • Soyoung Park

    (BK21 Plus Project of the Graduate School of Earth Environmental Hazard System, Pukyong National University, Busan 48513, Korea)

  • Se-Yeong Hamm

    (Department of Geological Sciences, Pusan National University, Busan 46241, Korea)

  • Hang-Tak Jeon

    (Department of Geological Sciences, Pusan National University, Busan 46241, Korea)

  • Jinsoo Kim

    (Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea)

Abstract

This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m 3 /d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1157-:d:103367
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    References listed on IDEAS

    as
    1. Saro Lee & Chang-Wook Lee, 2015. "Application of Decision-Tree Model to Groundwater Productivity-Potential Mapping," Sustainability, MDPI, vol. 7(10), pages 1-17, September.
    2. Gutiérrez, Álvaro Gómez & Schnabel, Susanne & Lavado Contador, J. Francisco, 2009. "Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies," Ecological Modelling, Elsevier, vol. 220(24), pages 3630-3637.
    3. repec:iwt:bosers:h040193 is not listed on IDEAS
    4. 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.
    5. repec:iwt:bosers:h039769 is not listed on IDEAS
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    3. Simona Ioana Ghita & Andreea Simona Saseanu & Rodica-Manuela Gogonea & Catalin-Emilian Huidumac-Petrescu, 2018. "Perspectives of Ecological Footprint in European Context under the Impact of Information Society and Sustainable Development," Sustainability, MDPI, vol. 10(9), pages 1-25, September.

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