Advanced machine learning models for robust prediction of water quality index and classification

ABSTRACT This study presents an in-depth analysis of machine learning (ML) techniques for predicting water quality index and water quality classification using a dataset containing water quality metrics such as temperature, specific conductance, salinity, dissolved oxygen, depth, pH, and turbidity from multiple monitoring stations. Data preprocessing included imputation for missing values, feature scaling, and categorical encoding, ensuring balanced input features. This research evaluated artificial neural networks, decision trees, support vector machines, random forests, XGBoost, and long short-term memory (LSTM) networks. Results demonstrate that XGBoost and LSTM significantly outperformed other models, with…

• DO
  Deanship of Scientific Research, King Khalid University

  Award: RGP 1/219/45
• KK
  King Khalid University