Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning

Yijun Lin, Nikhit Mago, Yu Gao, Yaguang Li, Yao Yi Chiang, Cyrus Shahabi, José Luis Ambite

Research output: Chapter in Book/Report/Conference proceedingConference contribution

98 Scopus citations

Abstract

Forecasting spatially correlated time series data is challenging because of the linear and non-linear dependencies in the temporal and spatial dimensions. Air quality forecasting is one canonical example of such tasks. Existing work, e.g., auto-regressive integrated moving average (ARIMA) and artificial neural network (ANN), either fails to model the non-linear temporal dependency or cannot effectively consider spatial relationships between multiple spatial time series data. In this paper, we present an approach for forecasting short-term PM 2.5 concentrations using a deep learning model, the geo-context based diffusion convolutional recurrent neural network, GC-DCRNN. The model describes the spatial relationship by constructing a graph based on the similarity of the built environment between the locations of air quality sensors. The similarity is computed using the surrounding “important” geographic features regarding their impacts to air quality for each location (e.g., the area size of parks within a 1000-meter buffer, the number of factories within a 500-meter buffer). Also, the model captures the temporal dependency leveraging the sequence to sequence encoder-decoder architecture. We evaluate our model on two real-world air quality datasets and observe consistent improvement of 5%-10% over baseline approaches.

Original languageEnglish (US)
Title of host publication26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
EditorsLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
PublisherAssociation for Computing Machinery
Pages359-368
Number of pages10
ISBN (Electronic)9781450358897
DOIs
StatePublished - Nov 6 2018
Externally publishedYes
Event26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
Duration: Nov 6 2018Nov 9 2018

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
Country/TerritoryUnited States
CitySeattle
Period11/6/1811/9/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Air Quality Forecasting
  • Deep Learning
  • PM2.5
  • Spatiotemporal Time Series Analysis

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