A Hybrid Approach to Water Quality Classification Using SVM and Xgboost Method

Akash B. Koli, Beldar Faijan Shaikh Akil, Lohar Bhavesh Kantilal, Pawar Darshan Madhukar and Patil Rushikesh Sanjay
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Akash B. Koli: Department of Computer Engineering, D.N. Patel College of Engineering, Shahada, Dist. Nandurbar, Maharashtra, India
Beldar Faijan Shaikh Akil: Department of Computer Engineering, D.N. Patel College of Engineering, Shahada, Dist. Nandurbar, Maharashtra, India
Lohar Bhavesh Kantilal: Department of Computer Engineering, D.N. Patel College of Engineering, Shahada, Dist. Nandurbar, Maharashtra, India
Pawar Darshan Madhukar: Department of Computer Engineering, D.N. Patel College of Engineering, Shahada, Dist. Nandurbar, Maharashtra, India
Patil Rushikesh Sanjay: Department of Computer Engineering, D.N. Patel College of Engineering, Shahada, Dist. Nandurbar, Maharashtra, India

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 6, 1006-1009

Abstract: This project focuses on the classification of waterquality using machine learning methods—Support Vector Machine (SVM) and XGBoost. The system uses various chemical indicators like pH, dissolved oxygen, turbidity, and conductivity to predict the water quality status. The dataset is preprocessed and important features are extracted before being passed into the models. After evaluating multiple models, XGBoost showed higher accuracy and robustness compared to SVM. The system aims to help environmental authorities monitor and improve water resources more effectively.

Date: 2025
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