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Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

A Correction to this article was published on 24 March 2022

This article has been updated

Abstract

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

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Data is available from the corresponding author with a formal request.

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Acknowledgements

The authors express their gratitude to the editorial board and reviewer for the efforts for suggestion and reviewing this paper. The authors also appreciate the editor for his cooperation during the review process.

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Manuscript writing and data analysis Akashdeep Bhardwaj, Akarsh Aggarwal, Supervision and conceptualization Muhammad Owais Khan, Vishal Dagar, Rafael Alvarado; Proofreading and reviewing Manoj Kumar, Muhammad Irfan, Ram Proshad. All authors have read and approved this manuscript.

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Correspondence to Muhammad Owais Khan.

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The authors declare no competing interests.

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Responsible Editor: Xianliang Yi

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Bhardwaj, A., Dagar, V., Khan, M.O. et al. Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring. Environ Sci Pollut Res 29, 46018–46036 (2022). https://doi.org/10.1007/s11356-022-19014-3

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  • DOI: https://doi.org/10.1007/s11356-022-19014-3

Keywords

  • Water quality monitoring
  • Real-time
  • IoT
  • Sensor
  • AI
  • Wireless
  • Embedded
  • Microcontroller