Karimi Hadi, Sahour Soheil, Khanbeyki Matin, Gholami Vahid, Sahour Hossein, Shahabi-Ghahfarokhi Sina, Mohammadi Mohsen
Department of Geological and Environmental Sciences, Western Michigan University, Kalamazoo, MI, 49008, USA.
Rouzbahan Institute of Higher Education, Sari, Iran.
Environ Monit Assess. 2024 Dec 4;197(1):21. doi: 10.1007/s10661-024-13506-0.
Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensemble learning model, for predicting groundwater quality in an unconfined aquifer located in northern Iran. The groundwater quality index (GWQI) from 250 wells was evaluated and classified. We considered various influential factors such as proximity to residential areas, evaporation, aquifer transmissivity, precipitation values, population density, distance to industrial centers, distance to water resources, and topography. Three different ML classifiers were employed to establish relationships between GWQI and the aforementioned factors: the AdaBoost classifier (ADA), quadratic discriminant analysis (QDA), and stacking ensemble learning (SEL). A novel model was introduced dubbed quadratic-ada-stacking ensemble learning (QA-SEL) to predict GWQI. The performance of these algorithms was evaluated through the receiver-operating characteristic (ROC) and multiple statistical efficiency indicators, including overall accuracy, precision, recall, and the F-1 score. All three ML algorithms displayed a high degree of accuracy in their GWQI predictions. Nonetheless, the QA-SEL method was identified as the most effective model due to its superior accuracy (overall accuracy, precision, recall = 0.95, 0.95, 0.96, ROC = 0.96, respectively). Following model optimization and testing, the QA-SEL model and a GIS were employed to map GWQI classes across the entire area. The produced GWQI map was validated by comparing the measured and predicted GWQI on the map. This study offers an economically efficient model for groundwater quality prediction, which can be replicated in other plains.
通过进行水样采集和实验室分析来评估地下水质量。实地测量成本高且耗时。本研究引入了一种基于机器学习（ML）的框架以及堆叠集成学习模型的创新应用，用于预测伊朗北部无压含水层的地下水质量。对来自250口井的地下水质量指数（GWQI）进行了评估和分类。我们考虑了各种影响因素，如与居民区的距离、蒸发量、含水层 transmissivity、降水量、人口密度、与工业中心的距离、与水资源的距离以及地形。采用三种不同的ML分类器来建立GWQI与上述因素之间的关系：AdaBoost分类器（ADA）、二次判别分析（QDA）和堆叠集成学习（SEL）。引入了一种名为二次 - ada - 堆叠集成学习（QA - SEL）的新模型来预测GWQI。通过接收器操作特征（ROC）和多个统计效率指标（包括总体准确率、精确率、召回率和F - 1分数）对这些算法的性能进行了评估。所有三种ML算法在其GWQI预测中都显示出高度的准确性。尽管如此，QA - SEL方法因其卓越的准确性（总体准确率、精确率、召回率分别为0.95、0.95、0.96，ROC为0.96）而被确定为最有效的模型。在模型优化和测试之后，使用QA - SEL模型和地理信息系统（GIS）绘制了整个区域的GWQI类别图。通过比较地图上测量的和预测的GWQI对生成的GWQI地图进行了验证。本研究提供了一种经济高效的地下水质量预测模型，可在其他平原地区复制。
