Imperial College London
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DrFangxinFang

Faculty of Engineering, Department of Earth Science & Engineering

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 1912f.fang

 
 
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Location

 

4.90Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cheng:2022:10.1029/2021MS002806,
author = {Cheng, M and Fang, F and Navon, IM and Zheng, J and Tang, X and Zhu, J and Pain, C},
doi = {10.1029/2021MS002806},
journal = {Journal of Advances in Modeling Earth Systems},
pages = {1--26},
title = {Spatio-temporal hourly and daily ozone forecasting in china using a hybrid machine learning model: autoencoder and generative adversarial networks},
url = {http://dx.doi.org/10.1029/2021MS002806},
volume = {14},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.
AU  - Cheng,M
AU  - Fang,F
AU  - Navon,IM
AU  - Zheng,J
AU  - Tang,X
AU  - Zhu,J
AU  - Pain,C
DO  - 10.1029/2021MS002806
EP  - 26
PY  - 2022///
SN  - 1942-2466
SP  - 1
TI  - Spatio-temporal hourly and daily ozone forecasting in china using a hybrid machine learning model: autoencoder and generative adversarial networks
T2  - Journal of Advances in Modeling Earth Systems
UR  - http://dx.doi.org/10.1029/2021MS002806
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000776466100017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002806
UR  - http://hdl.handle.net/10044/1/104957
VL  - 14
ER  -
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