Name
Title | ||
|---|---|---|
Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. |
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 PM2.5 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.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3274895.3274907 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Air Quality Forecasting, Spatiotemporal Time Series Analysis, PM2.5, Deep Learning | Built environment,Data mining,Time series,Computer science,Recurrent neural network,Autoregressive integrated moving average,Air quality index,Artificial intelligence,Deep learning,Artificial neural network,Moving average,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5889-7 | 7 | 0.70 |
References | Authors | |
11 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijun Lin | 1 | 11 | 2.23 |
| Nikhit Mago | 2 | 7 | 0.70 |
| Yu Gao | 3 | 61 | 15.12 |
| Yaguang Li | 4 | 177 | 10.43 |
| Yao-Yi Chiang | 5 | 360 | 31.33 |
| Cyrus Shahabi | 6 | 5010 | 411.59 |
| José Luis Ambite | 7 | 958 | 110.89 |