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Polluting fine dusts in South Korea which are mainly consisted of biomassburning and fugitive dust blown from dust belt is significant problem thesedays. Predicting concentrations of fine dust particles in Seoul is challengingbecause they are product of complicate chemical reactions among gaseouspollutants and also influenced by dynamical interactions between pollutants andmultiple climate variables. Elaborating state-of-art time series analysistechniques using deep learning, non-linear interactions between multiplevariables can be captured and used to predict future dust concentration. Inthis work, we propose the LSTM based model to predict hourly concentration offine dust at target location in Seoul based on previous concentration ofpollutants, dust concentrations and climate variables in surrounding area. Ourresults show that proposed model successfully predicts future dustconcentrations at 25 target districts(Gu) in Seoul.

Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM