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Conference Papers Year : 2020

Data assimilation for urban noise maps generated by a meta- model

Abstract

In an urban area, it is increasingly common to have access to both a simulated noise map and a sensor network. Merging these two types of information could improve the quality of urban noise maps. In this paper, a data assimilation algorithm is developed to combine data from both a noise map simulator and a network of acoustic sensors. One-hour noise maps are generated with a meta-model fed with hourly traffic and weather data. The data assimilation algorithm merges the simulated map with the sound level measurements into an improved noise map. The performance of this method relies on the accuracy of the meta-model, the input parameters selection and the model of the error covariance that describes how the errors of the simulated sound levels are correlated in space. The performance of the data assimilation is obtained with a leave-one-out cross- validation method. This method shows that the resulting sound maps achieve a reduction of about 30% of the root-mean-square error using 16 sound level meters over an area of 3km?, in our case study conducted in a district of Paris, France.
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Dates and versions

hal-03233662 , version 1 (26-05-2021)

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Cite

Antoine Lesieur, Vivien Mallet, Pierre Aumond, Arnaud Can. Data assimilation for urban noise maps generated by a meta- model. Forum Acusticum 2021, Dec 2020, Lyon / Virtual, France. pp.697-698, ⟨10.48465/fa.2020.0389⟩. ⟨hal-03233662⟩
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