Comparative Analysis of Time Series and Machine Learning Models for Air Quality Prediction Utilizing IoT Data

Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LSTM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10).