Performance analysis of the water quality index model for predicting water state using machine learning techniques

Existing water quality index (WQI) models assess using a range of classification schemes. Consequently, different methods provide number interpretations for the same properties that contribute to considerable amount uncertainty in correct quality. The aims this study were evaluate performance model order classify coastal correctly completely new scheme. Cork Harbour data was used study, which collected by Ireland's environmental protection agency (EPA). In present four machine-learning classifier algorithms, including support vector machines (SVM), Naïve Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), utilized identify best predicting classes widely seven WQI models, whereas three are recently proposed authors. KNN (100% 0% wrong) XGBoost (99.9% 0.1% algorithms outperformed accurately models. validation results indicate outperformed, accuracy (1.0), precision (0.99), sensitivity specificity F1 (0.99) score, predict Moreover, compared higher prediction accuracy, precision, sensitivity, specificity, score found weighted quadratic mean (WQM) unweighted root square (RMS) respectively, each class. findings showed WQM RMS could be effective reliable assessing terms classification. Therefore, helpful providing accurate information researchers, policymakers, research personnel monitoring more effectively.