The prediction of urban growth boundary based on the ANN-CA model: An application to Guangzhou

Abstract

Urban growth boundary (UGB) delineation is critical not only for China's urban planning policies, such as the ''three control lines'' of the Ministry of Natural Resources, but also for addressing global challenges related to sustainable urban development. This study contributes to the international discourse on urban growth management by developing an innovative artificial neural network-cellular automata (ANN-CA) model, tailored for cities experiencing rapid expansion. Using Guangzhou as a case study, we constructed an impact factor model that incorporates a wide range of factors, including urban spatial terrain, natural environment, current urban land classification, and industrial and economic conditions, along with the layout of modern service networks. The ANN-CA model was then employed to simulate urban spatial expansion and UGB delineation for the year 2030 under various constraints, such as strict protection zones and sustainable development scenarios. Our findings indicate that between 2020 and 2030, Nansha, Panyu, and Zengcheng districts will witness the most significant urban expansion, with respective area increases of 13.81 km2, 8.94 km2, and 5.8 km2, marking them as key growth areas. Furthermore, we propose that future urban expansion in Guangzhou should prioritize the southern and eastern regions, aligning with the city's strategic spatial objectives of ''moving east, expanding south, connecting west, and optimizing north.'' By emphasizing ecological protection and intensive land use, this study provides a robust framework for urban planning in Guangzhou and offers insights applicable to rapidly urbanizing regions worldwide.

Keywords

• Cellular automata
• Neural networks
• Growth boundary prediction
• Guangzhou city