ANN-CA Model Construction under SD Constraint: A Case Study on the Land-use Change Trend in Ya'an City

Abstract

Abstract Land systems are closely related to various factors, including social issues, national economies, natural environments, etc. However, the dynamic evolution simulation of land systems is quite complex. There are many problems in terms of land use in China, such as extensive development, significant regional differences, and imbalanced development. From this perspective, it is imperative to study systematic land use evolution according to local contexts. In this study, two methods that are commonly used to solve complexity issues— “top-down” system dynamics and “bottom-up” cellular automata—were combined to investigate the land use evolutionary trend in Ya’an City. Firstly, a system dynamics model of land resource use was constructed based on the causal relationship between land and macroscopic elements, and the quantitative future predictions of land use structures in Ya’an were obtained. Next, an ANN-CA model containing three steps—artificial neural network training, optimization of cellular automata parameters, and model calibration—was constructed. The land use scenario in Ya’an City in 2018 was simulated and the quantity and space accuracy verification were conducted. Finally, the spatial layout of land use in Ya’an City in 2028 and 2038 was predicted, and the land use evolution trend was studied using the model under the constraint of the quantitative prediction results of system dynamics. The results demonstrated the good simulation effect of the ANN-CA model under the SD constraint. The overall simulation accuracy of the model is the highest (93.93%) when the threshold of transformation is 0.8 and the diffusion coefficient is 1. In the future, the spatial distribution of different land types in Ya’an City will change slightly, and construction land presents an expansion law from the center to surrounding areas.