Markov-CA model using analytical hierarchy process and multiregression technique

• Published in: IOP conference series. Earth and environmental science 2014-06, Vol.20 (1), p.12008-12024
• Main Authors: Omar, N Q, Sanusi, S A M, Hussin, W M W, Samat, N, Mohammed, K S
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; Cellular automata; Land cover; Land use; Multiple criterion; Regression models; Social factors; Socioeconomic data; Socioeconomic factors; Socioeconomics; Transition rules; Urbanization
• ISSN: 1755-1315; 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/20/1/012008

The unprecedented increase in population and rapid rate of urbanisation has led to extensive land use changes. Cellular automata (CA) are increasingly used to simulate a variety of urban dynamics. This paper introduces a new CA based on an integration model built-in multi regression and multi-criteria evaluation to improve the representation of CA transition rule. This multi-criteria evaluation is implemented by utilising data relating to the environmental and socioeconomic factors in the study area in order to produce suitability maps (SMs) using an analytical hierarchical process, which is a well-known method. Before being integrated to generate suitability maps for the periods from 1984 to 2010 based on the different decision makings, which have become conditioned for the next step of CA generation. The suitability maps are compared in order to find the best maps based on the values of the root equation (R2). This comparison can help the stakeholders make better decisions. Thus, the resultant suitability map derives a predefined transition rule for the last step for CA model. The approach used in this study highlights a mechanism for monitoring and evaluating land-use and land-cover changes in Kirkuk city, Iraq owing changes in the structures of governments, wars, and an economic blockade over the past decades. The present study asserts the high applicability and flexibility of Markov-CA model. The results have shown that the model and its interrelated concepts are performing rather well.