Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

With the capacity to represent irregular geographical entities precisely, vector-based cellular automata (VCA) have been extensively employed in urban land change simulation at the land parcel level. However, while more driving factors are considered, modeling the complicated nonlinear relationship between land parcel attributes and multiple land-use changes is increasingly difficult. Moreover, in VCA, the driving factors are unstructured and cannot be directly modeled by traditional deep learning methods, which can only be applied to structured data processing. In order to address these problems, a new VCA model DF-VCA that adopts the deep forest (DF) algorithm for mining CA transition rules was proposed to simulate urban land-use change at the parcel level. The DF algorithm is a new deep learning method that can directly mine high-level features from unstructured data and obtain accurate land-use transition rules. The proposed DF-VCA model was applied to simulate the urban land change in Shenzhen, China. Compared with several traditional VCA models, the DF-VCA model achieved the best simulation performance at parcel-level (Figure of Merit = 39.88%), pattern-level (similarity = 96.47%), and community-level (correlation coefficient = 0.9269). The results show that the DF-VCA model with strong representative learning ability could simulate urban land change at fine scales precisely. Furthermore, the proposed DF-VCA model was applied in Shenzhen's future land change simulations to guide sustainable urban development.