Enhancing Water Sustainability with AI methods: Analysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach: Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach - Biplov Paneru; Bishwash Paneru; Sanjog Chhetri Sapkota (Universitas Komputer Indonesia, 2025)
Water quality monitoring is crucial globally, and particularly in Nepal, for ensuring safe drinking water and preventing waterborne illnesses. This study employs machine learning to analyze and forecast the seasonal Water Quality Index (WQI) of Nepalese well water. Hybrid models incorporating nested cross-validation were developed, evaluating algorithms such as CatBoost, Decision Trees, Logistic Regression, MLP-GRU, and LSTM-GRU. CatBoost achieved the highest classification accuracy (99.35%), while the LSTM-GRU hybrid excelled in capturing complex temporal patterns. Nested cross-validation demonstrated a robust 96.13% mean accuracy with low variance. Furthermore, SHAP analysis applied to a Support Vector Machine (SVM) model identified critical predictive factors. This research highlights the significant potential of machine learning in effectively managing and predicting water sustainability.