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

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 complex land-use changes. A multi-state artificial neural network based cell...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2013-05, Vol.27 (4), p.899-907
Main Authors: Ding, Wen-Juan, Wang, Ren-Qing, Wu, Da-Qian, Liu, Jian
Format: Article
Language:English
Subjects:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
Environmental impact
Land use
Math. Appl. in Environmental Science
Neural networks
Original Paper
Physics
Probability Theory and Stochastic Processes
Regression analysis
Rivers
Statistics for Engineering
Stochastic models
Waste Water Technology
Water Management
Water Pollution Control
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Summary: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 complex land-use changes. A multi-state artificial neural network based cellular automata (MANNCA) model and a multi-state autologistic regression based cellular automata (MALRCA) model were developed to simulate complex land-use changes in the Yellow River Delta during the period of 1992–2005. Relatively good conformity between simulated and actual land-use patterns indicated that the two models were able to simulate land-use dynamics effectively and generate realistic land-use patterns. The MANNCA model obtained higher fuzzy kappa values over MALRCA model at all the three simulation periods, which indicated that artificial neural networks could more effectively capture the complex relationships between land-use changes and a large set of spatial variables. Although the MALRCA model does have some advantages, the proposed MANNCA model represents a more effective approach to simulate the complex and nonlinear land-use evolutionary process.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-012-0624-7