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Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test

Author

Listed:
  • Jiyang Tian

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Chuanzhe Li

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Jia Liu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    State Key Laboratory of Hydrology-Water Resource and Hydraulirc Engineering, Hohai University, Nanjing 210098, China)

  • Fuliang Yu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Shuanghu Cheng

    (Bureau of Water Resources Survey of Heibei, Shijiazhuang 050031, China)

  • Nana Zhao

    (Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China)

  • Wan Zurina Wan Jaafar

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Prediction of the groundwater dynamics via models can help better manage the groundwater resources and guarantee their sustainable use. Three types of data-driven models are built for groundwater depth prediction in the plain of Shijiazhuang, the capital of Hebei Province in North China. The data-driven models include the Power Function Model (PFM), Back-Propagation Artificial Neural Network (BPANN) and Support Vector Machines (SVM) with two kernel functions of linear kernel function (LKF) and radial basis function (RBF). Five classes of factors (including 12 indices) are considered as potential model input variables. The Gamma Test (GT) is adopted in this study to help identify the relative importance of the input indices and tackle the tricky issue of the optimal input combinations for the data-driven models. The established models are evaluated in both fitting and testing procedures based on the root mean squared error (RMSE) and Nash-Sutcliffe efficiency (E) for different input combination schemes. The results show that SVM (RBF) performs the best. It is interesting to find that the natural factors (i.e., precipitation and evaporation) are less relevant to the groundwater depth variations. The methods used in this study have much significance for groundwater depth prediction in areas lacking hydrogeological data.

Suggested Citation

  • Jiyang Tian & Chuanzhe Li & Jia Liu & Fuliang Yu & Shuanghu Cheng & Nana Zhao & Wan Zurina Wan Jaafar, 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test," Sustainability, MDPI, vol. 8(11), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1076-:d:81242
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    References listed on IDEAS

    as
    1. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    2. Hamid Safavi & Mahdieh Esmikhani, 2013. "Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2623-2644, May.
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    2. Ahmadhon Akbarkhonovich Kamolov & Suhyun Park, 2021. "Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    3. Akram Rahbar & Ali Mirarabi & Mohammad Nakhaei & Mahdi Talkhabi & Maryam Jamali, 2022. "A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 589-609, January.
    4. Gislar E. Kifanyi & Julius M. Ndambuki & Samuel N. Odai, 2016. "A Quantitative Groundwater Resource Management under Uncertainty Using a Retrospective Optimization Framework," Sustainability, MDPI, vol. 9(1), pages 1-15, December.
    5. Yongho Ko & Seungwoo Han, 2017. "A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans," Sustainability, MDPI, vol. 9(4), pages 1-12, April.

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