Abstract

Groundwater is more prone to contamination due to its extensive usage. Different methods are applied to study vulnerability of groundwater including widely used DRASTIC method, SI and GOD. This study proposes a novel method of mapping groundwater vulnerability using machine learning algorithms. In this study, point extraction method was used to extract point values from a grid of 646 points of seven raster layer in the Al Khatim study area of United Arab Emirates. These extracted values were classified based on nitrate concentration threshold of 50 mg/L into two classes. Machine learning models were developed, using depth to water (D), recharge (R), aquifer media (A), soil media (S), topography (T), vadose zone (I) and hydraulic conductivity (C), on the basis of nitrate class. Classified ‘groundwater vulnerability class values’ were trained using 10-fold cross-validation, using four machine learning models which were Random Forest, Support Vector Machine, Naïve Bayes and C4. 5. Accuracy showed the model developed by Random Forest gained highest accuracy of 93%. Four groundwater vulnerability maps were developed from machine learning classifiers and was compared with base method of DRASTIC index. The efficiency, accuracy and validity of machine learning based models were evaluated based on Receiver Operating Characteristics (ROC) curve and Precision-Recall curve (PRC). The results proved that machine learning is an efficient tool to access, analyze and map groundwater vulnerability.

摘要

地下水由于其广泛使用而更容易受到污染。研究地下水脆弱性的方法包括广泛使用的 DRASTIC 方法、SI 和 GOD。本研究提出了一种使用机器学习算法绘制地下水脆弱性的新方法。本研究采用点提取方法从阿拉伯联合酋长国 Al Khatim 研究区的 7 个栅格图层的 646 个点的网格中提取点值。这些提取值基于 50 mg/L 的硝酸盐浓度阈值分为两类。使用深度到水 (D)、补给 (R)、含水层介质 (A)、土壤介质 (S)、地形 (T)、渗流带 (I) 和水力传导率 (C)，开发了机器学习模型。硝酸盐类的基础。使用随机森林、支持向量机、朴素贝叶斯和 C4 四种机器学习模型，使用 10 倍交叉验证对分类的“地下水脆弱性等级值”进行了训练。5.准确率显示随机森林开发的模型获得了93%的最高准确率。从机器学习分类器开发了四个地下水脆弱性图，并与 DRASTIC 指数的基本方法进行了比较。基于接收器操作特征 (ROC) 曲线和精确召回曲线 (PRC) 评估基于机器学习的模型的效率、准确性和有效性。结果证明，机器学习是获取、分析和绘制地下水脆弱性的有效工具。准确率显示，随机森林开发的模型获得了 93% 的最高准确率。从机器学习分类器开发了四个地下水脆弱性图，并与 DRASTIC 指数的基本方法进行了比较。基于接收器操作特征 (ROC) 曲线和精确召回曲线 (PRC) 评估基于机器学习的模型的效率、准确性和有效性。结果证明，机器学习是获取、分析和绘制地下水脆弱性的有效工具。准确率显示，随机森林开发的模型获得了 93% 的最高准确率。从机器学习分类器开发了四个地下水脆弱性图，并与 DRASTIC 指数的基本方法进行了比较。基于接收器操作特征 (ROC) 曲线和精确召回曲线 (PRC) 评估基于机器学习的模型的效率、准确性和有效性。结果证明，机器学习是获取、分析和绘制地下水脆弱性的有效工具。基于接收器操作特征（ROC）曲线和精确召回曲线（PRC）评估基于机器学习的模型的准确性和有效性。结果证明，机器学习是获取、分析和绘制地下水脆弱性的有效工具。基于接收器操作特征（ROC）曲线和精确召回曲线（PRC）评估基于机器学习的模型的准确性和有效性。结果证明，机器学习是获取、分析和绘制地下水脆弱性的有效工具。