Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh.

ABSTRACT: Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM_2.5 and PM₁₀, therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO_X, SO₂, CO, and O₃, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performe

SUBMITTER: Shahriar SA 

PROVIDER: S-EPMC7371793 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature