Prediction of drinking water quality with machine learning models: A public health nursing approach

AbstractObjectiveThe aim of this study is to use machine learning models to predict drinking water quality from a public health nursing approach.DesignMachine learning study.Sample“Water Quality Dataset” was used in the study. The dataset contains physical and chemical measurements of water quality for 2400 different water bodies. The process consists of four stages: Data processing with Synthetic Minority Oversampling Technique, hyperparameter tuning with 10‐fold cross‐validation, modeling and comparative analysis. 80% of the dataset is allocated as training data and 20% as test data. ML models logistic regression, K‐nearest neighbor, support vector machine, random forest, XGBoost, AdaBoost Classifier, Decision Tree algorithms were used for water quality prediction. Accuracy, precision, recall, F1 score and AUC performance metrics of ML models were compared. To evaluate the performance of the models, 10‐fold cross‐validation was used and a comparative analysis was performed. The p‐values of the models were also compared.ResultsN this study, where drinking water quality was predicted with seven different ML algorithms, it can be said that XGBoost and Random Forest are the best clas