Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping

被引:193
作者
Lee, Saro [1 ]
Kim, Yong-Sung [2 ]
Oh, Hyun-Joo [3 ]
机构
[1] Korea Inst Geosci & Mineral Resources KIGAM, Geosci Informat Ctr, Taejon 305350, South Korea
[2] Yooshin Engn Corp, Seoul 135936, South Korea
[3] Korea Inst Geosci & Mineral Resources KIGAM, Dept Overseas Mineral Resource, Taejon 305350, South Korea
关键词
Groundwater productivity potential; Weights-of-evidence; GIS; Korea; GEOGRAPHIC INFORMATION-SYSTEMS; MODEL; MANAGEMENT;
D O I
10.1016/j.jenvman.2011.09.016
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of >= 625 m(3)/d/m (Case 1) and T values of >= 3.79 m(2)/d (Case 3) corresponding to a yield (Y) of >= 500 m(3)/d, and SPC values of >= 3.75 m(3)/d/m (Case 2) and T values of >= 2.61 m(2)/d (Case 4) corresponding to a Y of >= 300 m(3)/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W+ and W- of each factor's rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 7120% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:91 / 105
页数:15
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