Deep spatial-temporal fusion network for fine-grained air pollutant concentration prediction

Air pollution is a serious environmental problem that has attracted much attention. Predicting air pollutant concentration can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing methods fail to model the temporal dependencies or have suffer from a weak ability to capture the spatial correlations of air pollutants. In this paper, we propose a general approach to predict air pollutant concentration, named DSTFN, which consists of a data completion component, a similar region selection component, and a deep spatial-temporal fusion network. The data completion component uses tensor decomposition method to complete the missing data of historical air quality. The similar region selection component uses region metadata to calculate the spatial similarity between regions. The deep spatial-temporal fusion network fuses urban heterogeneous data to capture factors affecting air quality and predict air pollutant concentration. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art models for air quality prediction.

[1]  Xiaoping Zhang,et al.  Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants , 2018, Journal of Geographical Sciences.

[2]  Stephen Grossberg,et al.  Recurrent neural networks , 2013, Scholarpedia.

[3]  Victor O. K. Li,et al.  An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data , 2017, IEEE Transactions on Big Data.

[4]  Rokjin J. Park,et al.  Urban air quality modeling with full O3–NOx–VOC chemistry: Implications for O3 and PM air quality in a street canyon , 2012 .

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[7]  Ujjwal Kumar,et al.  ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) , 2010 .

[8]  Jiachen Zhao,et al.  Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. , 2019, Chemosphere.

[9]  Griša Močnik,et al.  Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon , 2014 .

[10]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[11]  N. Kh. Arystanbekova,et al.  Application of Gaussian plume models for air pollution simulation at instantaneous emissions , 2004, Math. Comput. Simul..

[12]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[13]  Anikender Kumar,et al.  Forecasting of daily air quality index in Delhi. , 2011, The Science of the total environment.

[14]  D. Basak,et al.  Support Vector Regression , 2008 .