Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Civil Engineering, University of Ottawa, Ottawa, Canada.
Environ Sci Pollut Res Int. 2021 Sep;28(36):50525-50541. doi: 10.1007/s11356-021-13706-y. Epub 2021 May 7.
The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas.
本研究旨在利用 DRASTIC、修正 DRASTIC 以及三种统计二元模型（频率比（FR）、证据权（EBF）和证据权重（WOE））对伊朗 Sari-Behshahr 平原进行地下水脆弱性评估。2015 年共采集了 218 口井的硝酸盐浓度样本。数据集是使用硝酸盐浓度大于 50mg/L 的 109 口井的结果生成的。将硝酸盐数据分为两组，每组 70%（76 个位置作为训练数据集）用于建模，30%（33 个位置作为测试数据集）用于模型验证。最后，使用训练数据集生成了五张地下水潜在污染（GPP）图，然后使用测试数据集和接收者操作特征（ROC）方法进行评估。ROC 方法的结果表明，WOE 模型具有最高的预测能力，其次是 EBF、FR、修正 DRASTIC 和 DRASTIC 模型。获得的地图结果表明，高污染和极高污染潜力覆盖了研究区域的南部，那里有大城市。本研究的结果可以在其他地点复制，以识别地下水污染物易发生地区。
