Abstract
ABSTRACTLand use change models enable the exploration of the drivers and consequences of land use dynamics. A broad array of modeling approaches are available and each type has certain advantages and disadvantages depending on the objective of the research. This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic regression method (logit) and CA (CA-logit). Both approaches are used to calibrate the CA transition rules. Various geophysical and proximity factors are considered as urban expansion driving forces. Relative operating characteristic and a fuzzy map comparison are employed to evaluate the performance of the model. The evaluation processes highlight that the allocation ability of CA-SVMs slightly outperforms CA-logit approach. The result also reveals that the major urban expansion determinant is urban road infrastructure.
Highlights
Several land use change models are developed to explore the drivers of land use/land cover change and to simulate future land use patterns (e.g. Hallowell & Baran, 2013; Kryvobokov, Mercier, Bonnafous, & Bouf, 2015; Puertas, Henríquez, & Meza, 2014; Wang & Maduako, 2018)
This paper has been contributed to the few number of studies that calibrated transition rules of cellular automata (CA) models using support vector machines (SVMs)
Our model has been applied to Wallonia (Belgium) as a case study, but the model is generic and can be applied to other case studies
Summary
Several land use change models are developed to explore the drivers of land use/land cover change and to simulate future land use patterns (e.g. Hallowell & Baran, 2013; Kryvobokov, Mercier, Bonnafous, & Bouf, 2015; Puertas, Henríquez, & Meza, 2014; Wang & Maduako, 2018). In AB models, solutions have been designed to explore the emergent properties of systems with relatively simple behavioral rules representing individual agents. The urban-economic discrete choice models emerged from an integration of urban economic analysis with agents choices in the urban environment. Kryvobokov et al, 2015; Waddell, 2002) This application works with agents and integrates discrete choice approach and statistical methods to estimate model parameters (Ševčíková, Raftery, & Waddell, 2007). G. Mustafa et al, 2018b; Hu & Lo, 2007; Vermeiren, Van Rompaey, Loopmans, Serwajja, & Mukwaya, 2012) that help identify drivers behind land use change dynamics Another approach relies on statistical methods (e. g. Mustafa et al, 2018b; Hu & Lo, 2007; Vermeiren, Van Rompaey, Loopmans, Serwajja, & Mukwaya, 2012) that help identify drivers behind land use change dynamics
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