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- Title
Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks.
- Authors
Yang, Dawei; Zhao, Jiahui; Xu, Ping
- Abstract
Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development that emphasizes aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small- and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.
- Subjects
ARTIFICIAL neural networks; DEEP learning; PUBLIC spaces; CITIES &; towns; URBAN planning; METROPOLITAN areas
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 9, p3845
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14093845