An Improved Case-Based Reasoning Model for Simulating Urban Growth

Abstract

Developing urban growth models enables a better understanding and planning of sustainable urban areas. Case-based reasoning (CBR), in which historical experience is used to solve problems, can be applied to the simulation of complex dynamic systems. However, when applying CBR to urban growth simulation, problems such as inaccurate case description, a single retrieval method, and the lack of a time control mechanism limit its application accuracy. In order to tackle these barriers, this study proposes a CBR model for simulating urban growth. This model includes three parts: (1) the case expression mode containing the “initial state-geographical feature-result” is proposed to adapt the case expression to the urban growth process; (2) in order to improve the reliability of the results, we propose a strategy to introduce the “retrieval quantity” parameter and retrieve multiple similar cases; and (3) a time factor control method based on demand constraints is proposed to improve the power of time control in the algorithm. Finally, the city of Jixi was used as the study area for simulation, and when the “retrieval quantity” is 10, the simulation accuracy reaches 97.02%, kappa is 85.51, and figure of merit (FoM) is 0.1699. The results showed that the proposed method could accurately analyze urban growth.