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Air Quality Prediction of Relative Humidity Using IoT Sensors
Subject area: Science,Engineering and Technology · Area of research: Information Technology
Abstract
Air quality prediction is critical to environmental monitoring and public health management. Relative humidity, an important atmospheric parameter, significantly influences air quality and human comfort. This technical report focuses on the prediction of air quality based on relative humidity using IoT sensors and machine learning techniques, highlighting its significance and potential applications. IoT sensors offer a cost-effective and scalable solution for real-time data collection, including relative humidity measurements, in various locations and environments. These sensors provide continuous monitoring and enable the acquisition of high-resolution data on relative humidity levels. The report discusses the deployment of IoT sensors in indoor and outdoor settings, considering factors such as sensor placement, network architecture, and data transmission protocols. Machine learning algorithms are employed to analyse the collected data and develop predictive models for air quality based on relative humidity. These algorithms utilize historical air quality data of an Italian city, meteorological parameters, and relative humidity measurements as input to train and validate the models. The technical report explores various machine learning techniques, including regression models, decision trees, neural networks, and support vector machines, highlighting their capabilities in capturing complex relationships and dependencies between relative humidity and air quality parameters.
References
[1] J. Srishtishree, S. Mohana Kumar and Chetan Shetty, H. S. Saini et al. (2020), Air Quality Monitoring with IoT and Prediction Model using Data Analytics Innovations in Computer Science and Engineering, Lecture Notes in Networks and Systems 103.
[2] Van, N.H., Van Thanh, P., Tran, D.N. et al, (2023), A new model of air quality prediction using lightweight machine learning. Int. J. Environ. Sci. Technol. 20, 2983–2994.
[3] Castelli M, Clemente FM, Popovicˇ A, Silva S, Vanneschi L (2020) A machine learning approach to predict air quality in California. Hindawi 2020:23
[4] Bosnia H (2018) Air Quality Index (AQI) – Comparative study and assessment of an appropriate model for B&H," Academia
[5] Liu H, Li Q, Dongbing Y, Yu Gu (2019) Air quality index and air pollutant concentration prediction based on machine learning algorithms. Appl Sci 9(19):4069.
How to cite this paper
@article{1707179,
author = {Egboh Daniel Chukwunonso},
title = {Air Quality Prediction of Relative Humidity Using IoT Sensors},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {8},
pages = {406-412},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707179.pdf},
abstract = {Air quality prediction is critical to environmental monitoring and public health management. Relative humidity, an important atmospheric parameter, significantly influences air quality and human comfort. This technical report focuses on the prediction of air quality based on relative humidity using IoT sensors and machine learning techniques, highlighting its significance and potential applications. IoT sensors offer a cost-effective and scalable solution for real-time data collection, including relative humidity measurements, in various locations and environments. These sensors provide continuous monitoring and enable the acquisition of high-resolution data on relative humidity levels. The report discusses the deployment of IoT sensors in indoor and outdoor settings, considering factors such as sensor placement, network architecture, and data transmission protocols. Machine learning algorithms are employed to analyse the collected data and develop predictive models for air quality based on relative humidity. These algorithms utilize historical air quality data of an Italian city, meteorological parameters, and relative humidity measurements as input to train and validate the models. The technical report explores various machine learning techniques, including regression models, decision trees, neural networks, and support vector machines, highlighting their capabilities in capturing complex relationships and dependencies between relative humidity and air quality parameters.},
month = {February},
}