Cellular automata model as an intuitive approach to simulate complex land-use changes: an evaluation of two multi-state land-use models in the Yellow River Delta

摘要: Land-use changes are generally recognized as multi-scale complex systems with processes and driving factors operating at different scales. Traditional linear approaches could not adequately acquire the nonlinear features in land-use changes. A multi-state artificial neural network based cellular automata (MANNCA) model a autologistic regression (MALRCA) were developed to simulate Yellow River Delta during period of 1992–2005. Relatively good conformity between simulated actual patterns indicated that two models able dynamics effectively generate realistic patterns. The MANNCA obtained higher fuzzy kappa values over MALRCA all three simulation periods, which networks more capture relationships large set spatial variables. Although does have some advantages, proposed represents effective approach evolutionary process.