While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance‐based K‐nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree‐based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

中文翻译：

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地下水氟污染的随机模型：懒惰的学习者介绍

尽管它仍然是伊朗西北部马库平原的主要饮用水和灌溉水的主要来源，但该地区的地下水容易受到氟化物的污染。因此，需要精确预测地下水氟化物浓度的建模技术。当前的论文提出了几种新颖的数据挖掘算法，包括懒惰学习者[基于实例的K近邻（IBK）；本地加权学习（LWL）；和KStar]，基于树的算法（M5P）和元分类器算法[离散化回归（RBD）]来预测地下水中的氟化物浓度。利用几个地下水质量变量（例如，在2004年至2008年收集的143个样本中，对每个样本进行了测量，建立了几种预测地下水氟化物浓度的模型。完整的数据集分为两个子集：70％用于模型训练（校准）和30％用于模型评估（验证）。使用几种统计评估标准和三种视觉评估方法（即散点图，泰勒和小提琴图）对模型进行了验证。尽管Na ⁺和Ca ²⁺与氟化物具有最大的正相关和负相关性（r 分别为0.59和-0.39），不足以可靠地预测氟化物含量；因此，应将其他水质变量（包括那些与氟化物相关性较弱的变量）视为氟化物预测的输入。在氟化物污染预测方面，IBK模型优于其他模型，其次是KStar，RBD，M5P和LWL。就预测氟化物浓度值的峰值而言，RBD和M5P模型的准确性最差。本研究的结果可用于支持对水资源和地下水资源的实用和可持续管理。