Issue |
E3S Web Conf.
Volume 418, 2023
African Cities Conference (ACC 2023): A part of African Cities Lab 2023 Summit
|
|
---|---|---|
Article Number | 03008 | |
Number of page(s) | 5 | |
Section | Emerging Technologies and Applications to African Cities Issues | |
DOI | https://doi.org/10.1051/e3sconf/202341803008 | |
Published online | 07 December 2023 |
Overviewing the emerging methods for predicting urban Sprawl features
1 LRIT, Faculty of Sciences in Rabat, Mohammed V University in Rabat, Morocco.
2 Mohammed V University, High School of Technology, Sale Morocco
* e-mail: agbelinga@gmail.com
Urban sprawl, a common phenomenon characterized by uncontrolled urban growth, has far-reaching socio-economic and environmental implications. It’s a complex phenomenon, and finding a better way to tackle it is essential. Accurate simulation and prediction of urban sprawl features would facilitate decision-making in urban planning and the formulation of city growth policies. This article provides an overview of the techniques used to this end. Initially, it highlights the use of a certain category of so-called traditional methods, such as statistical models or classical machine learning methods. It then focuses particularly on the intersection of deep learning and urban sprawl modelling, examining how deep learning methods are being exploited to simulate and predict urban sprawl. I finally studies hybrid approaches that combine deep learning with agent-based models, cellular automata, or other techniques offer a synergistic way to leverage the strengths of different methodologies for urban sprawl modelling.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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