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Water contamination poses a severe risk to India’s water resources’ sustainability and can result in insufficient water supply for all residents, despite the country’s abundance of water resource. A significant portion of the population is impacted by different infections brought on by consuming contaminated water. Aquatic life forms, from fish to bacteria, depend on water for their home in addition to being essential for human nutrition. Water quality analysis is vital for maintaining ecological balance and biodiversity since aquatic ecosystems’ health is closely associated with the quality of the water they live in. In this project, machine learning is used to analyze and forecast the water quality using Python tools and the dataset that was fed into the model. This project aids in determining the potability of the water. Numerous factors, including pH, conductivity, hardness, particulates, turbidity, chloramines, sulfates, trihalomethanes, and organic carbons, are included in the dataset that affect the water’s cleanliness.

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Exploring Water Quality Prediction Using Machine Learning—An Efficient and Emerging Method of Evaluation

  • 会议论文

  • 发表日期：

    2025年

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  • Rajeshwarrao Arabelli,
  • Arasam Pooja,
  • Sripathi Sai Nanditha,
  • Kalluri Deepa,
  • Mondithoka Ajaybabu,
  • Syed Musthak Ahmed

摘要

Water contamination poses a severe risk to India’s water resources’ sustainability and can result in insufficient water supply for all residents, despite the country’s abundance of water resource. A significant portion of the population is impacted by different infections brought on by consuming contaminated water. Aquatic life forms, from fish to bacteria, depend on water for their home in addition to being essential for human nutrition. Water quality analysis is vital for maintaining ecological balance and biodiversity since aquatic ecosystems’ health is closely associated with the quality of the water they live in. In this project, machine learning is used to analyze and forecast the water quality using Python tools and the dataset that was fed into the model. This project aids in determining the potability of the water. Numerous factors, including pH, conductivity, hardness, particulates, turbidity, chloramines, sulfates, trihalomethanes, and organic carbons, are included in the dataset that affect the water’s cleanliness.