Deep learning methods evaluation to predict air quality based on Computational Fluid Dynamics

Xavier Jurado, Nicolas Reiminger, Marouane Benmoussa, José Vazquez, Cédric Wemmert

Abstract: Highlights•Methods to convert CFD inputs for neural networks are presented.•Deep Learning was successfully applied to predict pollutant dispersion.•Several Deep Learning models and loss were tested and compared.•The custom loss J3D outperformed binary cross entropy and mean squared error.•MultiResUnet is the best model for pollutant dispersion prediction.