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Bacterial prediction using internet of things (IoT) and machine learning.

Hamza Khurshid1, Rafia Mumtaz2, Noor Alvi1

  • 1School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.

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|January 28, 2022
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Summary

This study introduces an Internet of Things (IoT) system for real-time water quality monitoring and bacterial prediction. The system offers a more efficient and reliable alternative to traditional lab testing for ensuring public health.

Keywords:
Data analyticFecal coliformNeural networksReal-time data collectionWater quality index

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Area of Science:

  • Environmental Science
  • Computer Science
  • Public Health

Background:

  • Water quality is critical for public health, increasingly threatened by pollution and dwindling freshwater sources.
  • Traditional water quality monitoring methods are often slow, labor-intensive, and less reliable.
  • Developing countries face challenges with ineffective, human-dependent water quality management systems.

Purpose of the Study:

  • To develop a systematic and automated system for regular water quality monitoring and management.
  • To leverage the Internet of Things (IoT) for real-time, remote water quality measurements with minimal human intervention.
  • To enhance bacterial prediction accuracy and efficiency compared to conventional laboratory analyses.

Main Methods:

  • Deployment of IoT sensor nodes measuring parameters like dissolved oxygen, turbidity, pH, temperature, and total dissolved solids.
  • Data transmission via GSM modules to a central server for processing and analysis.
  • Water quality classification using indices and bacterial prediction via machine learning algorithms (SVM, Bayesian Regression).
  • Development of a web portal for real-time data visualization, including heat maps and infographics.

Main Results:

  • Support Vector Machines (SVM) and Bayesian Regression models demonstrated optimal performance in fecal coliform bacterial prediction, achieving low Mean Squared Error (MSE).
  • The IoT system successfully collected real-time water quality data and historical data for analysis.
  • A web portal was developed for effective data visualization and monitoring.

Conclusions:

  • The proposed IoT system offers a convenient, scalable, and cost-effective alternative to manual laboratory water quality testing.
  • Real-time monitoring and machine learning-based bacterial prediction can significantly improve water resource management and public health protection.
  • The system provides a portable hardware solution for remote and continuous water quality assessment.