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Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100\u20132000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design.<\/jats:p>","DOI":"10.3390\/ijgi14070244","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T08:23:22Z","timestamp":1750839802000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Machine Learning Framework for Urban Ventilation Corridor Identification Using LBM and Morphological Indices"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2361-9626","authenticated-orcid":false,"given":"Bu","family":"Yu","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"},{"name":"Hangzhou Meteorological Bureau, Hangzhou 310051, China"},{"name":"Hangzhou International Urbanology Research Center for Urban Governance Studies, Hangzhou 311121, China"},{"name":"Welsh School of Architecture, Cardiff University, Cardiff CF10 3NB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-381X","authenticated-orcid":false,"given":"Peng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"},{"name":"Welsh School of Architecture, Cardiff University, Cardiff CF10 3NB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1016\/j.buildenv.2008.06.013","article-title":"Policies and Technical Guidelines for Urban Planning of High-Density Cities\u2014Air Ventilation Assessment (AVA) of Hong Kong","volume":"44","author":"Ng","year":"2009","journal-title":"Build. 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