Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

Stochastic Environmental Research and Risk Assessment

Volumn 30, Issue 5, 2016, Pages 1387-1400

  Feng, Yongjiu ^a   Liu, Yan ^b   Batty, Michael ^c  

^a SHANGHAI OCEAN UNIVERSITY   (China)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Cellular automata (CA); Least squares support vector machines (LS SVM); Nonlinear transition rules; The Shanghai Qingpu Songjiang area; Urban growth

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELLULAR AUTOMATA; GEOGRAPHIC INFORMATION SYSTEMS; ITERATIVE METHODS; LAND USE; LEARNING SYSTEMS; MOBILE SECURITY; SUPPORT VECTOR MACHINES;

HIGH DIMENSIONAL SPACES; LEAST SQUARES SUPPORT VECTOR MACHINES; MODEL IMPLEMENTATION; NONLINEAR TRANSITION; SIMULATION ACCURACY; SPATIAL COMPLEXITY; SPATIAL CONFIGURATION; URBAN CELLULAR AUTOMATON;

URBAN GROWTH;

CELLULAR AUTOMATON; GIS; LEAST SQUARES METHOD; MACHINE LEARNING; SUPPORT VECTOR MACHINE; URBAN GROWTH;

CHINA; QINGPU; SHANGHAI; SONGJIANG;

EID: 84937690445     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-015-1128-z     Document Type: Article

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