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Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea

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  • Sunmin Lee

    (Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
    Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, Korea)

  • Yunjung Hyun

    (Department of Land and Water Environment Research, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, Korea)

  • Moung-Jin Lee

    (Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, Korea)

Abstract

Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future.

Suggested Citation

  • Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1678-:d:215626
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    References listed on IDEAS

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    2. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.
    3. Atul Kumar & Malay Pramanik & Shairy Chaudhary & Mahabir Singh Negi & Sylvia Szabo, 2023. "Geospatial multi-criteria evaluation to identify groundwater potential in a Himalayan District, Rudraprayag, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1519-1560, February.
    4. Aliasghar Azma & Esmaeil Narreie & Abouzar Shojaaddini & Nima Kianfar & Ramin Kiyanfar & Seyed Mehdi Seyed Alizadeh & Afshin Davarpanah, 2021. "Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique," Sustainability, MDPI, vol. 13(7), pages 1-18, March.
    5. Lamya Ouali & Lahcen Kabiri & Mustapha Namous & Mohammed Hssaisoune & Kamal Abdelrahman & Mohammed S. Fnais & Hichame Kabiri & Mohammed El Hafyani & Hassane Oubaassine & Abdelkrim Arioua & Lhoussaine , 2023. "Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco," Sustainability, MDPI, vol. 15(5), pages 1-28, February.

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