Побудова інтелектуальної моделі для прогнозування великодисперсного пилу (РМ10)

Abstract

In this article presents the development of an intelligent model for forecasting the concentration of coarse particulate matter (PM10) in the atmospheric air of Vinnytsia city. The study applies a systems analysis approach in combination with machine learning methods, specifically using the Random Forest algorithm. The model was implemented on the Kaggle platform using the Python programming language and libraries such as Scikit-learn, Pandas, and Matplotlib. The resulting model enables automated forecasting of PM10 levels and can be used in air quality monitoring systems to support decision-making in the field of environmental safety.