Water Quality Prediction Using Machine Learning Models

This research investigates the use of machine learning models to predict Water Quality Index (WQI) and Water Quality Classification (WQC), essential for monitoring and managing water resources. A dataset with key water quality parameters was used to train classification models like Random Forest, XGBoost, Gradient Boosting, and AdaBoost, along with regression models like K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Grid search with cross-validation optimized model performance, and preprocessing steps like mean imputation and normalization were applied. The Gradient Boosting model achieved the highest classification accuracy (99.5%), while MLP achieved the best regression results. Despite the promising results, the study highlights challenges such as computational complexity and overfitting risks, proposing innovations like explainable AI, advanced data imputation, and AutoML for future improvements.