Abstract：
Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML\'s superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.
摘要：
水在维持人类和其他生物体的生命中起着重要作用。地下水质量分析已成为必然,由于水资源污染和全球变暖的增加。本研究使用机器学习（ML）模型来预测水质指数（WQI）和水质分类（WQC）。在Ranipet工业走廊附近收集了40个地下水样本，并对水文地球化学和重金属污染进行了分析。WQC预测采用随机森林(RF)，梯度增强(GB)，决策树(DT)，和K最近邻(KNN)模型，WQI预测使用极端梯度提升(XGBoost)，支持向量回归量(SVR)，射频,和多层感知器(MLP)模型。采用网格搜索法,通过F1评分对ML模型进行评价,准确度,召回，精度,WQC的马修斯相关系数(MCC)和决定系数(R2)，平均绝对误差(MAE)，均方误差(MSE)，和WQI的中位数绝对百分比误差(MAPE)。WQI结果表明，研究区的地下水质量很差，不适合饮用或灌溉。RF模型的性能指标在预测WQC（精度=97％）和WQI（R2=91.0％）方面都非常出色，优于其他模型，强调ML在地下水质量评估中的优越性。研究结果表明，与地下水质量评估研究中使用的常规技术相比，ML模型表现良好，并且具有更好的准确性。
