Reliable and Efficient Model for Water Quality Prediction and Forecasting

Water quality is a crucial aspect of environmental and public health. Hence, its assessment is of paramount importance. This research paper aims to leverage machine learning models to classify water quality based on a comprehensive dataset. The dataset contains various water quality indicators, and the primary objective is to predict whether the water is safe or not to consume or use. This research evaluates the performance of diverse machine learning algorithms, such as Decision Trees, Random Forest, Logistic Regression, Support Vector Machines, and more for comparative analysis. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the models' effectiveness in classifying water quality. The Random Forest algorithm gave the best performance with an accuracy of 95.08%, an F1-Score of 94.69%, a Precision of 90.48%, a Recall of 93.10%, and an AUC score of 0.91. A comparative plot for the ROC AUC curve is also plotted between the various machine learning models used. Feature importance, which can help identify which water quality parameters have the greatest impact on predicting water quality outcomes, is also found in the research work.