Review on Advances in Machine and Deep Learning Methods for Forecasting Air Pollution

Abstract

This study aims to evaluate the impact of ML and DL approaches on air pollution predictions. Air pollution is becoming an increasingly pressing environmental and public health concern, and this study focuses on their predictive power in particular. The purpose of this research is to examine different methods for capturing the intricate patterns and correlations observed in air pollution data over time. ML and DL methodologies, including decision trees, support vector machines, CNNs, and RNNs. This study investigates the ways in which these methodologies improve the precision and dependability of predictions by utilising diverse data sources and facilitating real-time forecasting. In addition, it addresses persistent obstacles such as restricted data accessibility and the ability to interpret models, suggesting that researchers, politicians, and technology developers work together as a solution. By combining different data sources and allowing for real-time applications, the results imply that using ML and DL algorithms substantially improves the precision of air pollution prediction. Data trustworthiness and deep learning model comprehensibility are two ongoing issues. The future of weather forecasts, especially in densely populated urban areas, appears bright with the combination of atmospheric models, data from high-resolution sensors, and advanced machine learning algorithms. Lessening air pollution and its negative impacts on people and the planet calls for concerted action and fresh approaches.