A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure.

ABSTRACT: Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring

SUBMITTER: Gong X 

PROVIDER: S-EPMC10829730 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature