State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China; College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China.
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China.
Chemosphere. 2022 Sep;303(Pt 3):135265. doi: 10.1016/j.chemosphere.2022.135265. Epub 2022 Jun 9.
Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.
尽管地下水（GW）潜力分区对水资源管理可能有益，但目前在世界上包括巴基斯坦奎达谷在内的多个地方都缺乏这种分区。由于人口增长和工业发展不断增加，全球各地正在不加区分地使用 GW。鉴于 GW 潜力对可持续增长的重要性，本研究使用了 16 个 GW 驱动因素，通过使用包括人工神经网络（ANN）、随机森林（RF）、支持向量机（SVM）、K-最近邻（KNN）、朴素贝叶斯（NB）和极端梯度提升（XGBoost）在内的六种机器学习算法（MLA）来评估其效果。GW 产量数据被收集并分为 70%用于训练，30%用于验证。GW 产量的训练数据与 MLA 以及 GW 驱动变量相结合，然后使用接收者操作特征（ROC）曲线和验证数据来检查预测结果。在六种 ML 算法中，ROC 曲线显示 XGBoost、RF 和 ANN 模型表现良好，准确率分别为 98.3%、96.8%和 93.5%。此外，还使用平均绝对误差（MAE）、均方根误差（RMSE）、F 分数和相关系数来评估模型的准确性。对水文化学数据进行了评估，并计算了水质指数（WQI）。最后，使用 MLA 的输出和 WQI 创建了 GW 生产力潜力（GWPP）地图，因为它们可以识别不同的分类区域，政府和其他机构可以利用这些区域来定位新的 GW 井，并为岩石地形的水资源管理提供基础。
