  Polluting fine dusts in South Korea which are mainly consisted of biomass
burning and fugitive dust blown from dust belt is significant problem these
days. Predicting concentrations of fine dust particles in Seoul is challenging
because they are product of complicate chemical reactions among gaseous
pollutants and also influenced by dynamical interactions between pollutants and
multiple climate variables. Elaborating state-of-art time series analysis
techniques using deep learning, non-linear interactions between multiple
variables can be captured and used to predict future dust concentration. In
this work, we propose the LSTM based model to predict hourly concentration of
fine dust at target location in Seoul based on previous concentration of
pollutants, dust concentrations and climate variables in surrounding area. Our
results show that proposed model successfully predicts future dust
concentrations at 25 target districts(Gu) in Seoul.
