RESUMEN
This study aims to produce dynamic noise maps based on a noise model and acoustic measurements. To do so, inverse modeling and joint state-parameter methods are proposed. These methods estimate the input parameters that optimize a given cost function calculated with the resulting noise map and the noise observations. The accuracy of these two methods is compared with a noise map generated with a meta-model and with a classical data assimilation method called best linear unbiased estimator. The accuracy of the data assimilation processes is evaluated using a "leave-one-out" cross-validation method. The most accurate noise map is generated computing a joint state-parameter estimation algorithm without a priori knowledge about traffic and weather and shows a reduction of approximately 26% in the root mean square error from 3.5 to 2.6 dB compared to the reference meta-model noise map with 16 microphones over an area of 3 km2.
RESUMEN
Urban noise mapping generally consists of simulating the emission and attenuation of noise in an area by following rules such as common noise assessment methods. The computational cost makes these models unsuitable for applications such as uncertainty quantification, where thousands of simulations may be required. One solution is to replace the model with a meta-model that reproduces the expected noise levels with highly reduced computational costs. The strategy is to generate the meta-model in three steps. The first step is to generate a training sample exploring the large dimension model's inputs set. The second step is to reduce the dimension of the outputs. In the third step, statistical interpolators are defined between the projected values of the training sample over the reduced space of the outputs. Radial basis functions or kriging are used as interpolators. The meta-model was built using the open source software NoiseModelling. This study compares the proximity of the meta-model outputs to the model outputs against the reduced basis, the class of the kriging covariance function, and the training sample size. Simulations using the meta-model are more than 10 000 times faster than the model while maintaining the main behavior.