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Freshwater quality is crucial for maintaining healthy ecosystem and human well-being. Seawater intrusion due to natural processes or human activities can significantly degrade water quality in coastal regions. This study is aimed to predict water quality based on measurable parameters for improved water management. Water quality index is characterized by a number of factors, such as pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Saltness, Dissolved Oxygen (DO), Chlorides, Sulphates, Alkalinity, Ammonia, Nitrites, and Nitrates. For a good drinking water, the quality must range between 80–100. This work will highlight the capability of Machine Learning methods to enhance Water Quality Index (WQI) and facilitate more informed decision making in water quality management. The end users of this application will be water resource managers, public health officials, Agricultural, Industrial water users, Municipal water supply authorities.

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Smart Water Management: Predicting Water Quality Index with Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • V. Ajay Kumar,
  • G. Bala Gayathri,
  • P. Siddardha,
  • M. Tharun Kumar

摘要

Freshwater quality is crucial for maintaining healthy ecosystem and human well-being. Seawater intrusion due to natural processes or human activities can significantly degrade water quality in coastal regions. This study is aimed to predict water quality based on measurable parameters for improved water management. Water quality index is characterized by a number of factors, such as pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Saltness, Dissolved Oxygen (DO), Chlorides, Sulphates, Alkalinity, Ammonia, Nitrites, and Nitrates. For a good drinking water, the quality must range between 80–100. This work will highlight the capability of Machine Learning methods to enhance Water Quality Index (WQI) and facilitate more informed decision making in water quality management. The end users of this application will be water resource managers, public health officials, Agricultural, Industrial water users, Municipal water supply authorities.