Land use change; urban expansion; cellular automata; supported vector machines; logistic regression; Wallonia
Abstract :
[en] Land 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 supported 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 paper also reveals that the major urban expansion determinant is urban road infrastructure.
Research Center/Unit :
LEMA
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Mustafa, Ahmed Mohamed El Saeid ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Rienow, Andreas; Ruhr-University Bochum
Saadi, Ismaïl ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Cools, Mario ; Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité
Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire
Language :
English
Title :
Comparing support vector machines with logistic regression for calibrating cellular automata land use change models
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