IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i6p1678-d215626.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/6/1678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/6/1678/
    Download Restriction: no
    ---><---

    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. Satiprasad Sahoo & Selva Balaji Munusamy & Anirban Dhar & Amlanjyoti Kar & Prahlad Ram, 2017. "Appraising the Accuracy of Multi-Class Frequency Ratio and Weights of Evidence Method for Delineation of Regional Groundwater Potential Zones in Canal Command System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4399-4413, November.
    3. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    5. Saro Lee & Soo-Min Hong & Hyung-Sup Jung, 2017. "A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea," Sustainability, MDPI, vol. 9(1), pages 1-15, January.
    6. C. J. Vörösmarty & P. B. McIntyre & M. O. Gessner & D. Dudgeon & A. Prusevich & P. Green & S. Glidden & S. E. Bunn & C. A. Sullivan & C. Reidy Liermann & P. M. Davies, 2010. "Global threats to human water security and river biodiversity," Nature, Nature, vol. 467(7315), pages 555-561, September.
    7. Miltiadis D. Lytras & Vijay Raghavan & Ernesto Damiani, 2017. "Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 1-10, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nagehan İlhan & Ayşegül Demir Yetiş & Mehmet İrfan Yeşilnacar & Ayşe Dilek Sınanmış Atasoy, 2022. "Predictive modelling and seasonal analysis of water quality indicators: three different basins of Şanlıurfa, Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3258-3292, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    2. Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    3. Patricia Ordóñez de Pablos & Miltiadis Lytras, 2018. "Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness," Sustainability, MDPI, vol. 10(6), pages 1-7, June.
    4. Yung Yau & Wai Kin Lau, 2018. "Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    5. 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.
    6. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    7. Vasile Gherheș & Ciprian Obrad, 2018. "Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    8. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    9. Yi-Zeng Hsieh & Shih-Syun Lin & Yu-Cin Luo & Yu-Lin Jeng & Shih-Wei Tan & Chao-Rong Chen & Pei-Ying Chiang, 2020. "ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    10. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    11. Samuel Asumadu Sarkodie & Maruf Yakubu Ahmed & Phebe Asantewaa Owusu, 2022. "Global adaptation readiness and income mitigate sectoral climate change vulnerabilities," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    12. Yuichiro Yoshida & Han Soo Lee & Bui Huy Trung & Hoang-Dung Tran & Mahrjan Keshlav Lall & Kifayatullah Kakar & Tran Dang Xuan, 2020. "Impacts of Mainstream Hydropower Dams on Fisheries and Agriculture in Lower Mekong Basin," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
    13. Cai, Benan & Long, Chengjun & Du, Qiaochen & Zhang, Wenchao & Hou, Yandong & Wang, Haijun & Cai, Weihua, 2023. "Analysis of a spray flash desalination system driven by low-grade waste heat with different intermittencies," Energy, Elsevier, vol. 277(C).
    14. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    15. Antonio J. Castro & Cristina Quintas-Soriano & Jodi Brandt & Carla L. Atkinson & Colden V. Baxter & Morey Burnham & Benis N. Egoh & Marina García-Llorente & Jason P. Julian & Berta Martín-López & Feli, 2018. "Applying Place-Based Social-Ecological Research to Address Water Scarcity: Insights for Future Research," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    16. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    17. Maria Vincenza Ciasullo & Orlando Troisi & Mara Grimaldi & Daniele Leone, 2020. "Multi-level governance for sustainable innovation in smart communities: an ecosystems approach," International Entrepreneurship and Management Journal, Springer, vol. 16(4), pages 1167-1195, December.
    18. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    19. Qiting Zuo & Yixuan Diao & Lingang Hao & Chunhui Han, 2020. "Comprehensive Evaluation of the Human-Water Harmony Relationship in Countries Along the “Belt and Road”," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4019-4035, October.
    20. Paul L. G. Vlek & Asia Khamzina & Hossein Azadi & Anik Bhaduri & Luna Bharati & Ademola Braimoh & Christopher Martius & Terry Sunderland & Fatemeh Taheri, 2017. "Trade-Offs in Multi-Purpose Land Use under Land Degradation," Sustainability, MDPI, vol. 9(12), pages 1-19, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1678-:d:215626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.