MACHINE LEARNING BASED PREDICTION OF ATMOSPHERIC POLLUTION FOR ENVIRONMENTAL MANAGEMENT

Abstract

This investigation investigates the utilization of machine learning algorithms to predict air pollution levels in order to facilitate environmental management. The study develops predictive models that are capable of identifying complex patterns in pollution trends by analyzing historical air quality and meteorological data, including temperature, humidity, wind speed, and concentrations of pollutants such as PM₂.¹ and PM₁.₂. Regression models, decision trees, and ensemble methods are among the alternative machine learning algorithms that produce predictions that surpass the precision of conventional statistical methods. The proposed methodology enables governments and environmental organizations to implement measures that reduce emissions, protect public health, and improve long-term urban and environmental planning by facilitating the early estimation of pollution levels.