situations where the model is applied outside its training domain and a new LES run may be necessary to obtain reliable results. Furthermore, we demonstrate that it is possible to detect concept drift, i.e. It clearly outperforms the dummy model which would predict constant concentrations for all locations (multiplicative minimum RMSE (mRMSE) of 0.76 vs. Our results suggest that in general, log-linear regression gives the best and most robust performance on new independent data.

We also test the regression models on an independent testing dataset.

Regression models from 10 different model families are trained and a cross-validation methodology is used to evaluate their performance and to find the best set of features needed to reproduce the LES outputs.

We study the performance of regression models and discuss how to detect situations where the models are applied outside their training domain and their outputs cannot be trusted. In this study, regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards. Running large-eddy simulations (LESs) can be burdensome and computationally too expensive from the application point of view, for example, to support urban planning.

Bayes test models