Kamaliha, Arash (2025) Urban Airflow Field Prediction Using Sparse Sensors: A Hybrid Approach with Reduced-Order Model, Temporal Forecasting, and Multi-Task Learning. Masters thesis, Concordia University.
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Abstract
Urban centers worldwide are becoming ever denser, increasing the need for rapid and accurate wind modeling to ensure pedestrian comfort, optimize air quality management, and enable safe and efficient urban air mobility (UAM) operations. Traditional methods for modeling wind flow, such as computational fluid dynamics (CFD) simulations and field measurements, are computationally intensive and typically depend on dense sensor networks, which limits their practicality for real-time wind monitoring across large urban areas. The current thesis proposes an integrated approach for the fast reconstruction of the wind velocity fields in urban environments using only online sensor signals. The model development phase involves three main components: First, a bidirectional long short-term memory (BILSTM) network is trained by using the offline sensors signals to capture the temporal evolution of the sensor readings. Second, the sparsity-promoting dynamic mode decomposition (SP-DMD) algorithm is employed to extract the important spatial pattern (modes) and their associated dynamic coefficients from the high-dimensional wind velocity data. Finally, a multi-task learning (MTL) network, is developed to map sensor signals to the real (growth/decay) and imaginary (oscillatory) components of these dynamic coefficients at each time step. During the prediction phase, real-time and out-of-sample sensor signals from the same locations are fed into the BILSTM model to forecast future measurements. The predicted signals are then input into the trained MTL network to infer the corresponding dynamic coefficients. Finally, the wind field is reconstructed in real-time via a linear combination of the SP-DMD modes and their coefficients. The proposed framework was validated using high-fidelity CFD simulations for wind velocity flow around an isolated high-rise building. Results demonstrated that the proposed method has a robust capability for online reconstruction of precise urban flow fields. Additionally, during the online prediction phase, the model performance is evaluated for different level of noise in sensor signals, highlighting the model’s robustness and generalizability. These findings suggest that the integrated modeling strategy can substantially lower operational costs and holds promise as a surrogate model for applications in next-generation urban planning and autonomous aerial navigation systems.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Kamaliha, Arash |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Building Engineering |
| Date: | December 2025 |
| Thesis Supervisor(s): | Nasiri, Fuzhan |
| Keywords: | Urban Wind Flow Prediction, Reduced Order Modeling, Multi-task Learning, Dynamic Mode Decomposition, Computational Fluid Dynamics, Bidirectional Long Short-Term Memory |
| ID Code: | 996604 |
| Deposited By: | Arash Kamaliha |
| Deposited On: | 29 Jun 2026 14:25 |
| Last Modified: | 29 Jun 2026 14:25 |
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