@article{jurczyski2025evaluating,
  title = {Evaluating the Potential of Random Forest Regression for PM2.5 Modeling Within Environmental Safety Decision Support Systems},
  author = {Dawid JURCZYŃSKI},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/46AI/2025/4637925/},
  journal = {Communications of International Proceedings},
  volume = 2025 (34),
  doi = doi.org/10.5171/2025.4637925,
  abstract = {Progressive urbanization and increased air pollution emissions pose a significant challenge for modern safety engineering, especially in terms of forecasting and mitigating environmental risks1. This paper presents a machine learning-based approach to modeling PM2.5 particulate matter concentrations using open environmental and meteorological data from the OpenWeatherMap platform. The aim of the study was to develop a model to support decision-making in environmental safety systems through early detection of potential air pollution episodes. The study used a Random Forest model with parameter optimization and cross-validation, as well as time feature transformations to account for the cyclical nature of atmospheric phenomena. The analysis of the results showed a high correlation between the predictions and the actual PM2.5 concentrations, with a coefficient of determination R² above 0.8 in most of the analyzed time intervals. The results confirm the effectiveness of the proposed approach in identifying trends and environmental anomalies that may pose a threat to public health. The developed model can be a component of intelligent environmental safety management systems2 and a basis for further research on the integration of artificial intelligence with IoT infrastructure and urban air quality monitoring systems.},
  keywords = {PM2.5 particulate matter, Random Forest Regression, safety decision support systems},
  note = Article ID: 4637925
}