Urban airflow field prediction using sparse sensors
A hybrid data-driven approach with reduced-order modeling
Bibliographic Data
| ID | 21231364 |
|---|---|
| Authors | Arash Kamaliha (0009-0008-9427-2539, Concordia University), Fuzhan Nasiri (0000-0002-7423-0621, Concordia University, corresponding author) |
| Year | 2026 |
| Volume | 148 |
| Pages | 107609 |
| Publication date | 2026-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2026.107609 |
| OpenAlex | W7165198745 |
| Language | EN |
| Citations received | 1 |
| References cited | 67 |
Urban airflow field modeling can provide cities with monitoring solutions to enhance pedestrian/cyclists comfort, improve air quality, and enhance urban wind energy generation yield. In addition, it could support a safe and efficient deployment of urban air mobility (UAM) supporting the next generation of faster and more sustainable means of urban transport reducing the congestion and traffic. However, real-time wind monitoring across urban areas remains a significant challenge due to spatial and temporal variations and uncertainties. This study presents an integrated framework that serves as a surrogate model for the real-time reconstruction and prediction of wind velocity fields around a simplified urban building configuration. The approach relies exclusively on online sensor measurements and employs reduced-order modeling techniques for dimensionality reduction, thereby improving computational efficiency. The offline development of the model integrates three principal components: (1) a bidirectional long short-term memory (BILSTM) network trained on airflow sensor data to capture temporal dynamics, (2) a sparsity-promoting dynamic mode decomposition (SP-DMD) for extracting dominant spatial modes of airflow, and (3) a multi-task learning (MTL) network to map sensor signals. The proposed framework was assessed against high-fidelity CFD simulations of wind flow around a high-rise urban building. The results indicate that the proposed three-stage method reliably reconstructs accurate urban airflow fields in real time under varying levels of sensor noise, and demonstrate its feasibility for sparse-sensor reconstruction under the tested sensor-placement configurations. Moreover, these findings demonstrate that the proposed approach can significantly reduce operational/computational costs and shows strong potential as a surrogate tool for next-generation of urban airflow monitoring
Airflow · Computational fluid dynamics · Deep learning · Field (mathematics) · Flow (mathematics) · Software deployment · Wind speed · Wireless sensor network · Aerodynamics and Fluid Dynamics Research · Meteorological Phenomena and Simulations · Wind and Air Flow Studies
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |