Fluoride in groundwater has been found to pose a severe public health threat in two villages (Karataş and Sarım) of western Sanliurfa in the southeastern Anatolia region of Turkey, where many cases of fluorosis, which detrimentally affects the teeth and bones, have been reported. Analysis of fluoride in drinking water is usually accomplished using various chemical methods, but while these techniques produce accurate and reliable results, they are expensive, labor-intensive, and cumbersome. In this study, a more cost-effective alternative, based on machine learning methods, is introduced. In this case, artificial neural network (ANN), support vector machine (SVM), and Naïve Bayes classifiers are utilized. Furthermore, a novel feature selection and ranking method known as Normalized Weighted Voting Map (NWVM) is presented. In Fisher discrimination power (FDP) scores, X-ray fluorescence (XRF) variables have higher discrimination power potential than X-Ray diffraction (XRD) attributes, the most salient feature being Zr (0.464) and CaO (219.993) from XRD and XRF, respectively. When the XRD and XRF parameters are classified separately for the effect of NWVM ranking scores on the fluoride values and dental fluoride in groundwater, CaO, SiO₂, MgO, Fe₂O₃, P₂O₅, and K₂O (for XRF) and Quartz and Zr (for XRD) present a stronger effect. In addition, when looking at the effects among themselves, the first order is the same XRF parameters and then the XRD parameters. Experiments revealed that XRF constituents including CaO, SiO₂, MgO, P₂O₅, and K₂O have higher class discrimination power than the XRD variables.

在土耳其东南部安那托利亚地区的Sanliurfa西部的两个村庄（Karataş和Sarım），发现地下水中的氟化物构成了严重的公共卫生威胁，据报道，那里有许多氟中毒病例，对牙齿和骨骼造成有害影响。通常使用多种化学方法来完成饮用水中氟化物的分析，但是尽管这些技术产生了准确而可靠的结果，但它们昂贵，费力且麻烦。在这项研究中，介绍了一种基于机器学习方法的更具成本效益的替代方案。在这种情况下，将使用人工神经网络（ANN），支持向量机（SVM）和朴素贝叶斯分类器。此外，提出了一种新颖的特征选择和排序方法，称为归一化加权投票图（NWVM）。在Fisher鉴别力（FDP）评分中，X射线荧光（XRF）变量具有比X射线衍射（XRD）属性更高的鉴别力潜力，其中最突出的特征是XRD和XRF的Zr（0.464）和CaO（219.993） ， 分别。当针对NWVM等级分数对地下水中的氟化物值和牙科氟化物的影响分别对XRD和XRF参数进行分类时，CaO，SiO_{如图2所示}，MgO，Fe ₂ O ₃，P ₂ O ₅和K ₂ O（对于XRF）和石英和Zr（对于XRD）表现出更强的作用。另外，当查看它们之间的效果时，第一顺序是相同的XRF参数，然后是XRD参数。实验表明，包括CaO，SiO ₂，MgO，P ₂ O ₅和K ₂ O在内的XRF成分具有比XRD变量更高的类别区分能力。