地下水脆弱性评估是预测和预防地下水污染的有用工具。这项研究的目标是DRASTIC,证据信念函数(EBF),和逻辑回归(LR)模型来评估喀布尔含水层的脆弱性,阿富汗国家。城市蔓延的增长,地下水过度开采,缺乏合适的城市污水系统作为人为来源,是增加研究区域硝酸盐等地下水污染物的主要潜力。漏洞地图是根据各种有效因素开发的,包括高度,坡度(上升百分比),方面,曲率,土地利用类型,排水密度,距离河流,年平均降水量,净充值,地质/岩性单位,渗流带的影响,含水层介质,到水的深度(不饱和区),饱和区,drawdown,和水力传导性。为了识别地下水污染,2018年硝酸盐浓度数据的空间变化被认为是地下水污染的指示。根据描述性统计,选择2.65mg/l(硝酸盐图像素值的中位数)的值作为阈值,以区分污染的发生和不发生。选择地下水水质数据,并随机分为两个数据集进行培训和验证,包括70%和30%,分别。基于受试者工作特征(ROC)曲线和曲线下面积(AUC)计算成功率和预测率曲线,以估计模型的效率。EBF成功率的ROC-AUC,LR,DRASTIC模型估计为67%,66%,52%,分别。此外,EBF预测率的ROC-AUC,LR,和DRASTIC模型获得61%,63%,55%,分别。根据各模型中硝酸盐平均浓度与脆弱性指数的相关性,与LR和DRASTIC模型相比,EBF模型与当前已开发的脆弱区最兼容,因为它是人类在实际条件下改变环境的作用。
The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This
study targets the
DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the
study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65 mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and
DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and
DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and
DRASTIC models.