Research Article
Learning Air Pollution with Bidirectional LSTM RNN
@INPROCEEDINGS{10.4108/eai.21-6-2018.2276560, author={Weitian Tong and Lixin Li and Xiaolu Zhou and Andrew Hamilton}, title={Learning Air Pollution with Bidirectional LSTM RNN}, proceedings={Workshop on Environmental Health and Air Pollution}, publisher={EAI}, proceedings_a={IWEHAP}, year={2018}, month={9}, keywords={spatiotemporal interpolation air pollution deep learning bidirectional lstm rnn}, doi={10.4108/eai.21-6-2018.2276560} }- Weitian Tong
Lixin Li
Xiaolu Zhou
Andrew Hamilton
Year: 2018
Learning Air Pollution with Bidirectional LSTM RNN
IWEHAP
EAI
DOI: 10.4108/eai.21-6-2018.2276560
Abstract
An accurate understanding of air pollutants in a continuous space-time domain by spatiotemporal interpolation is critical for meaningful assessment of the quantitative relationship between the public health and perennial environmental exposures. Existing spatiotemporal interpolation algorithms are usually based on unrealistic assumptions by restricting the interpolation models to the ones with explicit and simple mathematical descriptions, thus neglecting plenty of hidden yet critical influence factors. We developed an efficient deep-learning-based spatiotemporal interpolation algorithm which can generate more accurate estimation for air pollution on a large geographic scale and over a long time period. The experimental results demonstrate the efficacy and efficiency of our novel algorithm.
