Data assimilation for urban noise mapping with a meta-model

Accurately predicting dynamic noise levels in urban environments is non-trivial. This study aims to optimally combine both simulated and empirical data. Acoustic data from microphone arrays, traffic and weather data was merged with a simulated noise map, created with a statistical emulator tool (meta-model). Each hour, a noise map is generated by the meta-model with the measured traffic and weather data. This map is algorithmically merged with the measured readings to form a new composite map. The resulting analyzed map is the best linear unbiased estimator under certain assumptions. The performance is evaluated with leave-one-out cross-validation. The performance of the method depends on the accuracy of the meta-model, the input parameters of the meta-model and the structure of the error covariances between the simulated noise level errors. With 16 microphones over an area of 3 km(2), this new method achieves a reduction of 30% of the root-mean-square error when compared to ameta-model only. (C) 2021 Elsevier Ltd. All rights reserved.