Cartographie avancée des indicateurs de bruit urbain par des techniques d’apprentissage automatique

Advanced urban noise indicator mapping by machine learning techniques

In order to more accurately estimate perception and health outcomes related to environmental noise exposure, indicators beyond long-term equivalent sound pressure levels are needed (such as statistical levels, number of events, psycho-acoustical indices, etc). Predicting these more advanced noise indicators is challenging, especially in the urban environment.

This Phd will rely on noise mapping codes combined with (simplified) dynamic traffic estimations as a basis for the physical modelling. After issuing the Environmental Noise Directive in Europe, efforts have been undertaken to standardize and develop traffic source power models and outdoor sound propagation models. The “CNOSSOS” model is currently the preferred method to make noise maps and to report them to the European Commission. But such maps should be considered as “strategic” and are thus not well suited to predict the effect of noise abatements or to predict sound pressure levels at micro-environments (e.g. at a shielded side of a building). And although such strategic models often lack the necessary accuracy, in the urban environment, they are yet computationally very costly. Note that better propagation codes are available like the physically more correct HARMONOISE model, but employing this model would blow up simulation times even more.