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Water contamination poses a global threat to public health, ecosystems, and economic stability. This paper reviews the integration of Machine Learning (ML) and the Internet of Things (IoT) in advancing early detection mechanisms for water contamination. Traditional methods rely on manual sampling, causing delays and limited coverage. IoT-enabled smart water monitoring systems offer real-time, comprehensive data. ML algorithms, particularly supervised and unsupervised learning, automate analysis, predicting abnormal patterns indicative of contamination. This integration enables proactive, data-driven decision-making, fostering adaptive systems that self-optimize over time. ML and IoT pinpoint contamination sources, allowing targeted remediation. Despite challenges like data quality and interpretability, embracing this technology-driven paradigm promises transformative improvements in water management, mitigating risks and ensuring access to safe water globally.

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The Role of Machine Learning and IoT in Early Detection of Water Contamination

  • 章节

  • 发表日期：

    2025年

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  • Tarun Kumar Vashishth,
  • Vikas Sharma,
  • Kewal Krishan Sharma,
  • Bhupendra Kumar,
  • Sachin Chaudhary,
  • Rajneesh Panwar

摘要

Water contamination poses a global threat to public health, ecosystems, and economic stability. This paper reviews the integration of Machine Learning (ML) and the Internet of Things (IoT) in advancing early detection mechanisms for water contamination. Traditional methods rely on manual sampling, causing delays and limited coverage. IoT-enabled smart water monitoring systems offer real-time, comprehensive data. ML algorithms, particularly supervised and unsupervised learning, automate analysis, predicting abnormal patterns indicative of contamination. This integration enables proactive, data-driven decision-making, fostering adaptive systems that self-optimize over time. ML and IoT pinpoint contamination sources, allowing targeted remediation. Despite challenges like data quality and interpretability, embracing this technology-driven paradigm promises transformative improvements in water management, mitigating risks and ensuring access to safe water globally.