A Comparative Analysis of Artificial Intelligence Methods for Air Quality Prediction on Construction Sites

This study focuses on predicting air pollutants on construction sites, which is an essential aspect for preserving the health of workers and people who live nearby. We carried out through data pre-processing, handling missing values and transforming categorical variables. The focus is forecasting different key air pollutants like PM2.5, PM10, SO2, NO, CO2, and O3. Hence, to achieve this, we compare different statistical, machine learning and deep learning approaches. The novelties of the proposed work from the previous works are: (i) the prediction of multiple pollutants, (ii) the use of multiple predictive models, and (iii) a larger prediction window of 12 hours. We compare the models by computing the Root Mean Squared Error and the R2 to assess their performances. This study provides a comparative analysis of well-known models in the literature for predicting air quality in construction sites. In conclusion, the findings show that training LSTM models can significantly enhance air pollution predictions, providing valuable insights for improving environmental monitoring and forecasting accuracy.