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首页> 外文期刊>Journal of Hydroinformatics >Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater
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Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater

机译：基于GIS的证据，加权因子和统计指标的应用在地下水的空间建模中的应用

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摘要

The present research aims at applying three geographic information system (GIS)-based bivariate models, namely, weights of evidence (WOE), weighting factor (WF), and statistical index (SI), for mapping of groundwater potential for sustainable groundwater management. The locations of wells with groundwater yields more than 11 m(3)/h were selected for modeling. Then, these locations were grouped into two categories with 70% (52 locations) in a training dataset to build the model and 30% (22 locations) in a testing dataset to validate it. Conditioning factors, namely, altitude, slope degree, plan curvature, slope aspect, rainfall, soil, land use, geology, distance from fault, and distance from river were selected. Finally, the three achieved maps were compared using area under receiver operating characteristic (ROC) and area under the ROC curve (AUC). The ROC method result showed that the SI model better fitted the training dataset (AUC = 0.747) followed by WF (AUC = 0.742) and WOE (AUC = 0.737). Results of the testing dataset show that the WOE model (AUC = 0.798) outperforms SI (AUC = 0.795) and WF (AUC = 0.791). According to the WF model, altitude and rainfall had the highest and lowest impacts on groundwater well potential occurrence, respectively. With regard to Friedman test, the difference in performances of these three models was not statistically significant.
机译：本研究旨在应用三个地理信息系统（GIS）的双重变频模型，即证据重量（WOE），加权因子（WF）和统计指标（SI），用于绘制可持续地下水管理的地下水潜力。选择具有地下水的孔的位置超过11m（3）/ h以进行建模。然后，将这些位置分为两个类别，其中在训练数据集中为70％（52个位置），以在测试数据集中构建模型和30％（22个位置）以验证它。调节因素，即海拔高度，坡度，平面曲率，坡面，降雨，土壤，土地利用，地质，距离距离以及河流的距离以及距离河流。最后，使用在ROC曲线（AUC）下的接收器操作特性（ROC）和面积下的区域进行比较三个达到的地图。 ROC方法结果表明，SI模型更好地拟合训练数据集（AUC = 0.747），然后是WF（AUC = 0.742）和WOE（AUC = 0.737）。测试数据集的结果表明，WOE模型（AUC = 0.798）优于SI（AUC = 0.795）和WF（AUC = 0.791）。根据WF模型，海拔和降雨量分别对地下水潜在发生的最高和最低影响。关于弗里德曼测试，这三种模型的性能的差异在统计学上没有统计学意义。

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