Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test
AbstractPrediction 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
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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.
Tian J, Li C, Liu J, Yu F, Cheng S, Zhao N, Wan Jaafar WZ. Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test. Sustainability. 2016; 8(11):1076.Chicago/Turabian Style
Tian, Jiyang; Li, Chuanzhe; Liu, Jia; Yu, Fuliang; Cheng, Shuanghu; Zhao, Nana; Wan Jaafar, Wan Z. 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test." Sustainability 8, no. 11: 1076.
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