Estimating ground-level PM_2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh.

ABSTRACT: Fine particulate matter (PM_2.5) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM_2.5 concentrations. However, statistical models have shown inconsistency in PM_2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree,

SUBMITTER: Islam ARMT 

PROVIDER: S-EPMC9961308 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature