Faster and more accurate noise mapping combining mettamodeling and dat assimilation
Résumé
Urban noise mapping can be sped up replacing the traditional noise mapping model with a meta-model. In the present study, in the framework of the CENSE project, a meta-model was obtained by training on 2000 simulations of NoiseModelling software, on part of Paris. It is based on dimension reduction of inputs and outputs of NoiseModelling. The reduced model is then approximated by a statistical emulator. The resulting meta-model can closely reproduce any NoiseModelling noise map for Paris under any typical traffic condition, but with a computational time about 10000 times lower. The meta-model is then employed to assimilate acoustic measurements from 16 locations. State estimation, inverse modeling and joint state-parameter estimation are compared and evaluated using cross validation. The most accurate noise map is obtained with joint state-parameter estimation, without a priori knowledge about traffic and weather.