Journal article 21044 views 171 downloads
Machine learning-based rapid response tools for regional air pollution modelling
D. Xiao,
F. Fang,
J. Zheng,
C.C. Pain,
I.M. Navon,
Dunhui Xiao 
Atmospheric Environment, Volume: 199, Pages: 463 - 473
Swansea University Author:
Dunhui Xiao
-
PDF | Accepted Manuscript
Download (1.26MB)
DOI (Published version): 10.1016/j.atmosenv.2018.11.051
Abstract
A parameterised non-intrusive reduced order model (P-NIROM) based on proper orthogonal decomposition (POD) and machine learning methods has been firstly developed for model reduction of pollutant transport equations. Our motivation is to provide rapid response urban air pollution predictions and con...
| Published in: | Atmospheric Environment |
|---|---|
| ISSN: | 13522310 |
| Published: |
2019
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa46447 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
A parameterised non-intrusive reduced order model (P-NIROM) based on proper orthogonal decomposition (POD) and machine learning methods has been firstly developed for model reduction of pollutant transport equations. Our motivation is to provide rapid response urban air pollution predictions and controls. The varying parameters in the P-NIROM are pollutant sources. The training data sets are obtained from the high fidelity modelling solutions (called snapshots) for selected parameters (pollutant sources, here) over the parameter space . From these training data sets, the machine learning method is used to generate the relationship between the reduced solutions and inputs (pollutant sources) over . Furthermore a set of hyper-surface functions associated with each POD basis function is constructed for representing the fluid dynamics over the reduced space. The accuracy of the P-NIROM is highly dependent on the quality of the training set, here obtained from the high fidelity model. Over existing machine learning methods, the P-NIROM algorithm proposed here has the advantages that (1) it is combined with NIROM, thus providing rapid and reasonably accurate solutions; and (2) it is a robust and efficient approach for representation of any parametrised partial differential equations as the model parameters/inputs vary. In this study, we demonstrate the way how to implement the P-NIROM for the pollutant transport equation (but not limited to due to its robustness). Its predictive capability is illustrated in a three-dimensional (3-D) simulation of power plant plumes over a large region in China, where the varying parameters are the emission intensity at three locations. Results indicate that in comparison to the high fidelity model, the CPU cost is reduced by factor up to five orders of magnitude while reasonable accuracy remains. |
|---|---|
| Keywords: |
Machine learning, Finite element, Proper orthogonal decomposition, Reduced order modelling, Air pollution |
| College: |
Faculty of Science and Engineering |
| Start Page: |
463 |
| End Page: |
473 |