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Sustainability 2016, 8(11), 1076; doi:10.3390/su8111076

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

1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
State Key Laboratory of Hydrology-Water Resource and Hydraulirc Engineering, Hohai University, Nanjing 210098, China
3
Bureau of Water Resources Survey of Heibei, Shijiazhuang 050031, China
4
Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China
5
Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Torretta
Received: 26 August 2016 / Revised: 10 October 2016 / Accepted: 18 October 2016 / Published: 25 October 2016
(This article belongs to the Section Sustainable Use of the Environment and Resources)
View Full-Text   |   Download PDF [3067 KB, uploaded 25 October 2016]   |  

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. View Full-Text
Keywords: groundwater dynamics prediction; data-driven models; Gamma Test; power function model; back-propagation artificial neural network; support vector machine groundwater dynamics prediction; data-driven models; Gamma Test; power function model; back-propagation artificial neural network; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tian, J.; Li, C.; Liu, J.; Yu, F.; Cheng, S.; Zhao, N.; Wan Jaafar, W.Z. Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test. Sustainability 2016, 8, 1076.

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