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 language | English (US) |
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Title of host publication | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 |
Editors | Li Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel |
Publisher | Association for Computing Machinery |
Pages | 359-368 |
Number of pages | 10 |
ISBN (Electronic) | 9781450358897 |
DOIs | |
State | Published - Nov 6 2018 |
Externally published | Yes |
Event | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States Duration: Nov 6 2018 → Nov 9 2018 |
Publication series
Name | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
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Other
Other | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 |
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Country/Territory | United States |
City | Seattle |
Period | 11/6/18 → 11/9/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Keywords
- Air Quality Forecasting
- Deep Learning
- PM2.5
- Spatiotemporal Time Series Analysis