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With the escalating concerns over air quality and how it affects people’s health and the environment, there is a pressing need for advanced monitoring and prediction systems to mitigate air pollution. This research paper delves into the integration of Internet of Things (IoT) technology and machine-learning algorithms to develop a comprehensive framework for real-time air-pollution visualizing and prediction. The proposed system incorporates IoT sensors such as MQ-7(CO); PM2.5 (particulate matter), MQ-135 (smoke, benzene) in different locations which gathers and transmit data to a platform (ThingSpeak) where Machine-Learning Model (Linear-Regression, Decision-Tree-Regressor, Random-Forest-Regressor, Logistic-regression, Decision-tree classifier, Random-Forest-Classifier, K-Nearest Neighbours) analyse the collected data and gives patterns, correlations and trends in air quality fluctuations ultimately yielding predicted output AQI value and forecasting the AQI value for next upcoming hours. The effectiveness of the aforementioned machine-learning strategies is compared using several performance metrics in this presented paper.

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Forecasting Air Quality: A Convergence of Machine Learning and IoT for Pollution Detection and Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Abhiraj Paul,
  • Amandeep Vasistha,
  • Kalpeswar Paul,
  • Subhadeep Karmakar,
  • Siddhanta Borah,
  • Navajit Saikia,
  • Niranjan Jyoti Borah

摘要

With the escalating concerns over air quality and how it affects people’s health and the environment, there is a pressing need for advanced monitoring and prediction systems to mitigate air pollution. This research paper delves into the integration of Internet of Things (IoT) technology and machine-learning algorithms to develop a comprehensive framework for real-time air-pollution visualizing and prediction. The proposed system incorporates IoT sensors such as MQ-7(CO); PM2.5 (particulate matter), MQ-135 (smoke, benzene) in different locations which gathers and transmit data to a platform (ThingSpeak) where Machine-Learning Model (Linear-Regression, Decision-Tree-Regressor, Random-Forest-Regressor, Logistic-regression, Decision-tree classifier, Random-Forest-Classifier, K-Nearest Neighbours) analyse the collected data and gives patterns, correlations and trends in air quality fluctuations ultimately yielding predicted output AQI value and forecasting the AQI value for next upcoming hours. The effectiveness of the aforementioned machine-learning strategies is compared using several performance metrics in this presented paper.