Ground-level ozone forecasting using data-driven methods

Accurate site-specific forecasting of hourly ground-level ozone concentrations is a key issue in air quality research nowadays due to increase of smog pollution problem. This paper investigates three emergent data-driven methods to address the complex nonlinear relationships between ozone and meteorological variables in Hamilton (Ontario, Canada). Three dynamic neural networks with different structures: a time-lagged feed-forward network, a …