End-to-end data-driven prediction of urban airflow and pollutant dispersion

TL;DR

This paper presents a comprehensive data-driven framework combining spectral decomposition, autoencoders, LSTM networks, and CNNs to accurately predict urban airflow and pollutant dispersion in street canyons, aiding urban environmental management.

Contribution

It introduces an end-to-end approach integrating multiple machine learning techniques for fast, accurate urban airflow and pollutant dispersion prediction from LES data.

Findings

The model accurately predicts instantaneous airflow and pollutant fields.

The approach effectively captures long-term statistical behavior.

The framework reduces computational costs compared to traditional simulations.

Abstract

Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned in the latent space using Long Short-Term Memory (LSTM) netowrks. Finally, the pollutant dispersion is estimated from the predicted velocity field through convolutional neural network that maps both fields. The results demonstrate the efficacy of the model in predicting the instantaneous as well as statistically stationary fields over long time horizon.