As a main type of urban construction land, urban-industrial land is used to provide the judging criteria for construction land scale in the planning period when urban population, industrial development, investment scale, and other conditions are uncertain in China; however, research on expected indicator such as urban-industrial land in overall land use plan mainly focuses on qualitative analysis; quantitative analysis research has not yet been carried out. Using MATLAB R2016a software modeling tools to establish GM (1, 1) model and RBF neural network model, respectively, this paper predicted the demand of urban-industrial land in Beijing-Tianjin-Hebei Urban Agglomeration. Comparing the predicated results with the actual value of urban-industrial land in Beijing, Tianjin, and 11 prefecture-level cities in Hebei Province, we determined the reasonable prediction model for urban-industrial land after testing the accuracy of the two prediction models. The results showed that the RBF neural network model was the more reasonable prediction model for urban-industrial land. Using the predicted results of the RBF neural network model, combining expected indicators of overall land use plan (2006-2020) in Beijing and Tianjin, as well as 11 prefecture-level cities in Hebei Province in the planning target year, determined remaining usable time of urban-industrial land. Finally, combined with the actual scale of urban-industrial land in 2015 and the predicated scale of urban-industrial land in 2020, the remaining usable time of each city's urban-industrial land was calculated in terms of the average annual growth rate of urban-industrial land from 2009 to 2015. According to the comparative relationship between the remaining usable time and the remaining time of the overall land use plan (5 years), urban-industrial lands were divided into three kinds of regulation zones: reasonable reduction zone, optimized adjustment zone, and core development zone. The policy implications for urban-industrial land in each regulation zone were also provided. This paper can provide reference for regulation zoning of urban-industrial land in developing countries and regions.
Keywords: Beijing-Tianjin-Hebei Urban Agglomeration; Demand prediction; RBF neural network model; Regulation; Urban-industrial land; Zoning.