Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran.
Department of Natural Resources and Member of Water Managements Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Environ Sci Pollut Res Int. 2023 Nov;30(54):115758-115775. doi: 10.1007/s11356-023-30530-8. Epub 2023 Oct 27.
In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best-Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chlorine (Cl), sulfate (SO), total dissolved solids (TDS), bicarbonate (HCO), electrical conductivity (EC), and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR), and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that the RF algorithm with values of MAE = 0.28, MSE = 0.12, RMSE = 0.35, and AUC = 0.93 was selected as the most optimal MLA. The Schoeller diagram also showed that various ion ratios, including NaK, Ca, Mg, Cl, and HCO+CO, in most of the sampled points had upward average values. Based on the results of the BWM method, it can be concluded that a great similarity was observed between the results of the RF algorithm and the classification of the BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources.
在伊朗，与其他发展中国家一样，地下水水质受到了严重威胁。因此，本研究旨在应用机器学习算法（MLAs）进行地下水质量建模（GQM），并使用最佳最差法（BWM）确定伊朗阿尔达比勒省的最佳算法。地下水质量参数包括钙（Ca）、镁（Mg）、钠（Na）、钾（K）、氯（Cl）、硫酸盐（SO）、总溶解固体（TDS）、碳酸氢盐（HCO）、电导率（EC）和酸度（pH）。接下来，使用 Python 编程语言对包括支持向量回归（SVR）、随机森林（RF）、决策树回归器（DTR）、K 近邻（KNN）、朴素贝叶斯、简单线性回归（SLR）和支持向量机（SVM）在内的七种 MLAs 进行了地下水质量建模。最后，使用 BWM 对 MLAs 的结果进行了验证。通过检查确定地下水质量建模中最佳算法的误差统计，结果表明，RF 算法的 MAE = 0.28、MSE = 0.12、RMSE = 0.35 和 AUC = 0.93，被选为最优化的 MLA。Schoeller 图也表明，在大多数采样点中，各种离子比，包括 NaK、Ca、Mg、Cl 和 HCO+CO，的平均值呈上升趋势。根据 BWM 方法的结果，可以得出结论，RF 算法的结果与 BWM 方法的分类之间存在很大的相似性。这些结果表明，根据地下水水质的水化学参数，超过 50%的研究区域水质较差。本研究的结果可以帮助管理者和规划者制定适合的管理模型，并为优化地下水资源的开采实施适当的策略。
