1. Source term estimation: variational method versus machine learning applied to urban air pollution
- Author
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Lopez-Ferber, Roman, Leirens, Sylvain, Georges, Didier, Département Systèmes (DSYS), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Université Grenoble Alpes (UGA)
- Subjects
advection-diffusion ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,neural network ,Source Detection ,3D-Var ,air pollution ,[SDE]Environmental Sciences ,Source Term Estimation ,variational methods - Abstract
International audience; 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.
- Published
- 2022