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Source term estimation: variational method versus machine learning applied to urban air pollution

Abstract : Source detection is a field of study gaining interest due to environmental concerns about air quality in populated areas. We developed a machine learning framework inspired by previous works on road traffic estimation, and compared it to a classical variational method under a unidimensional and stationary problem. We tested source reconstruction with datasets coming from 12 and 50 sensors with and without noise. Noise was set to follow a gaussian law with a dependent variance from the maximum measured value of a concentration profile. Both methods are reasonably robust to noise. The results reveal that the Neural Network used here, a multilayer perceptron, performs very well compared to the classical 3D-Var method.
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https://hal-cea.archives-ouvertes.fr/cea-03716399
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Submitted on : Thursday, July 7, 2022 - 2:00:15 PM
Last modification on : Saturday, July 9, 2022 - 3:07:05 AM

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  • HAL Id : cea-03716399, version 1

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Roman Lopez-Ferber, Sylvain Leirens, Didier Georges. Source term estimation: variational method versus machine learning applied to urban air pollution. CSC 2022 - IFAC Workshop on Control for Smart Cities, Jun 2022, Sozopol (virtual), Bulgaria. ⟨cea-03716399⟩

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