Data-Driven Air Quality Modeling: XGBoost Amplified with Generative Adversarial Networks

Abstract

In response to the escalating environmental concerns posed by air pollution, this research endeavors to present a thorough investigation into air quality prediction utilizing advanced machine learning techniques, specifically focusing on the integration of Generative Adversarial Networks (GANs). The study relies on an extensive dataset comprising historical air quality records, meteorological variables, and other relevant factors. Employing a diverse set of machine learning algorithms, including decision trees, XGBoost, support vector machines, random forests etc., along with GANs, and our aim is to construct robust models for accurate air quality prediction. The outcomes of this research shed light on the efficacy of machine learning, including GANs, in unraveling intricate patterns within air quality data. The developed models offer valuable insights for air quality management and inform public health initiatives. By contributing to the advancement of data-driven methodologies, particularly with the inclusion of GANs, this study plays a pivotal role in the realms of environmental monitoring and policy development.