Machine learning-based rapid response tools for regional air pollution modelling

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Title: Machine learning-based rapid response tools for regional air pollution modelling
Authors: Xiao, D
Fang, F
Pain, C
Navon, IM
Zheng, J
Item Type: Journal Article
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.
Issue Date: 15-Feb-2019
Date of Acceptance: 20-Nov-2018
URI: http://hdl.handle.net/10044/1/66479
DOI: https://dx.doi.org/10.1016/j.atmosenv.2018.11.051
ISSN: 1352-2310
Publisher: Elsevier
Start Page: 463
End Page: 473
Journal / Book Title: Atmospheric Environment
Volume: 199
Copyright Statement: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: RG80519
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Environmental Sciences
Meteorology & Atmospheric Sciences
Environmental Sciences & Ecology
Machine learning
Finite element
Proper orthogonal decomposition
Reduced order modelling
Air pollution
REDUCTION
0907 Environmental Engineering
0401 Atmospheric Sciences
Publication Status: Published
Online Publication Date: 2018-11-24
Appears in Collections:Faculty of Engineering
Earth Science and Engineering



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