Machine learning algorithms in air quality modeling

A. Masih

Volume 5, Issue 4 , October 2019, , Pages 515-534

https://doi.org/10.22034/GJESM.2019.04.10

Abstract
  Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have ...  Read More

Application of ensemble learning techniques to model the atmospheric concentration of SO2

A. Masih

Volume 5, Issue 3 , July 2019, , Pages 309-318

https://doi.org/10.22034/GJESM.2019.03.04

Abstract
  In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single ...  Read More