An Effective Air Pollution Prediction Model Using Machine Learning Algorithms

Abstract

Air pollution is a major environmental concern globally, with both developed and developing countries facing its impacts. In recent years, citizens and governments have become increasingly concerned about the effects of air pollution on human health and have proposed sustainable development initiatives to address this issue. In a recent study, air pollution data from the years 2020-2022 was collected from a secondary data source. The data set included six key input features, including SO2, PM2.5, CO, PM10, NO2, and O3 values. To analyse this data, various machine learning models were employed, including linear regression, multiple linear regression, KNN, random forest regression, decision tree regression, support vector regression, and artificial neural networks. To ensure the accuracy of the predictions, mean square error and R square were used to measure the absolute error and forecast precision. Additionally, the importance of each input feature in air pollution was investigated, providing valuable insights into the factors that contribute to air pollution levels. Overall, the use of machine learning algorithms in air pollution estimation and prediction has significant potential to improve our understanding of this critical environmental issue and inform effective strategies for addressing it.