Air Quality Prediction and Monitoring Using Machine Learning Algorithm and IOT

EasyChair Preprint 7575

Abstract

High amounts of harmful chemicals & particles in the atmosphere create health problems. They influence the planet's ecosystems, making air pollution the critical problem in human development in the previous century. As technology improves, scientists and environmental associations examine new ways to fight and control air pollution, resulting in new resolutions. Over the final decade, devices that can monitor and control pollution classes have evolved more accessible and small expensive. The Internet of Things (IoT) has spawned new approaches to pollution management. The purpose of the research described in this article was to apply machine learning to predict the behaviour of the air quality index. To test our model, they collected data from one of the IoT sensors. To explore temperature growth about pollution levels and other pollution sources, there provided Q-Learning and Fuzzy logic approaches. Finally, the results of the algorithms are displayed in terms of accuracy and error rate for two different methods. Finally, we find that the proposed Q-Learning method outperforms existing state-of-the-art classification algorithms in terms of accuracy.

Keyphrases: Air Quality Monitoring, Classification, IoT, data processing, machine learning, supervised learning

@booklet{EasyChair:7575,
  author    = {Hrishikesh Sonar and Vaibhavkumar Kagne and Vivek Khalane},
  title     = {Air Quality Prediction and Monitoring Using Machine Learning Algorithm and IOT},
  howpublished = {EasyChair Preprint 7575},
  year      = {EasyChair, 2022}}