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This research focuses on learning how to apply machine learning models in monitoring and predicting water quality. In particular, it emphasizes critical aspects of environmental management. The case study focuses on chemical parameters, where ammonia levels are the principal indicator. Data preprocessing is followed by exploratory analysis, training, and then evaluation. The models used in this experiment range from Decision Trees to Random Forests, Support Vector Machines, to boosting techniques such as Voting Classifiers, Boosting, LightGBM, and CatBoost. It is to be noted that the ensemble approaches do provide tremendous predictive gains, achieving accuracy levels above 97%. These approaches do give useful insights into detecting unsafe water conditions, which affects public health and environmental policy. This work highlights the potential role of machine learning to revolutionize a traditional water quality monitoring system by providing an efficient, automated real-time analysis for better decision-making.

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Harnessing Machine Learning for Accurate Water Quality Monitoring and Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Madira Srilatha,
  • Maridu Bhargavi,
  • Sanagapati Akanksha,
  • Manne Kirety Chowdary,
  • Biladugu Ramanjamma

摘要

This research focuses on learning how to apply machine learning models in monitoring and predicting water quality. In particular, it emphasizes critical aspects of environmental management. The case study focuses on chemical parameters, where ammonia levels are the principal indicator. Data preprocessing is followed by exploratory analysis, training, and then evaluation. The models used in this experiment range from Decision Trees to Random Forests, Support Vector Machines, to boosting techniques such as Voting Classifiers, Boosting, LightGBM, and CatBoost. It is to be noted that the ensemble approaches do provide tremendous predictive gains, achieving accuracy levels above 97%. These approaches do give useful insights into detecting unsafe water conditions, which affects public health and environmental policy. This work highlights the potential role of machine learning to revolutionize a traditional water quality monitoring system by providing an efficient, automated real-time analysis for better decision-making.