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Meta-modeling for urban noise mapping.

Antoine Lesieur1, Pierre Aumond2, Vivien Mallet1

  • 1ANGE, INRIA, 2 rue Simone Iff, 75012 Paris, France.

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Summary

This study introduces a faster meta-model for urban noise mapping, reducing computational costs by over 10,000 times. The new approach maintains accuracy for applications like uncertainty quantification in noise assessment.

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Area of Science:

  • Environmental acoustics
  • Computational modeling
  • Urban planning

Background:

  • Urban noise mapping typically uses computationally intensive simulation models.
  • High computational costs limit applications like uncertainty quantification requiring numerous simulations.

Purpose of the Study:

  • To develop a computationally efficient meta-model for urban noise mapping.
  • To assess the accuracy and speed benefits of the proposed meta-model.

Main Methods:

  • Generated a training sample for a high-dimensional input set.
  • Reduced the dimensionality of the model outputs.
  • Employed statistical interpolators (radial basis functions or kriging) for meta-model construction.
  • Utilized the open-source software NoiseModelling.

Main Results:

  • The meta-model achieved over 10,000 times faster simulations compared to the original model.
  • The meta-model successfully reproduced the main behavior of the full model.
  • Accuracy was compared against reduced basis, kriging covariance function class, and training sample size.

Conclusions:

  • Meta-models offer a significant reduction in computational cost for urban noise mapping.
  • The developed meta-model provides a viable solution for computationally demanding applications like uncertainty quantification.
  • The approach maintains the essential characteristics of complex noise simulations.