Deep learning-based urban morphology for city-scale environmental modeling

Pratiman Patel; Rajesh Kalyanam; Liu He; Daniel Aliaga; Dev Niyogi

doi:10.1093/pnasnexus/pgad027

Deep learning-based urban morphology for city-scale environmental modeling

PNAS Nexus. 2023 Feb 3;2(3):pgad027. doi: 10.1093/pnasnexus/pgad027. eCollection 2023 Mar.

Authors

Pratiman Patel^{1

2}, Rajesh Kalyanam³, Liu He¹, Daniel Aliaga¹, Dev Niyogi^{4

5}

Affiliations

¹ Department of Computer Sciences, Purdue University, 305 N University St, West Lafayette, 47907 IN, USA.
² Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra, India.
³ Research Computing, Purdue University, 155 S Grant St, West Lafayette, 47907 IN, USA.
⁴ Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, 2305 Speedway Stop C1160, Austin, 78712-1692 TX, USA.
⁵ Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.

Abstract

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

Keywords: WUDAPT; deep neural network; urban boundary layer; urban canopy parameters; urban climate; weather research and forecasting model.