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Conference Papers Year : 2022

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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Dates and versions

cea-03716399 , version 1 (07-07-2022)

Identifiers

  • HAL Id : cea-03716399 , version 1

Cite

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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