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.
Citations
IRE Journals:
Egboh Daniel Chukwunonso
"Air Quality Prediction of Relative Humidity Using IoT Sensors" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 406-412
IEEE:
Egboh Daniel Chukwunonso
"Air Quality Prediction of Relative Humidity Using IoT Sensors" Iconic Research And Engineering Journals, 8(8)
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