Spatially fine-grained air quality prediction based on DBU-LSTM

This paper proposes a general approach to predict the spatially fine-grained air quality. The model is based on deep bidirectional and unidirectional long short-term memory (DBU-LSTM) neural network, which can capture bidirectional temporal dependencies and spatial correlations from time series data. Urban heterogeneous data such as point of interest (POI) and road network are used to evaluate the similarities between urban regions. The tensor decomposition method is used to complete the missing historical air quality data of monitoring stations. We evaluate our approach on real data sources obtained in Beijing, and the experimental results show its advantages over baseline methods.