Modelling Land-use and Land-cover Changes Using Markov-CA, and Multiple Decision Making in Kirkuk City

Abstract

Abstract. This paper tries to highlight on the analysis of the land-use and land-cover changes in Kirkuk city / Iraq that's due to repeated changes in structures of governments, wars and economic blockade over the past three decades that drove to chaos. The Markov-cellular automata model is an important and powerful tool in simulation. In this article a new Markov-CA model was developed based on built-in multi-regression model and multi-criteria evaluation approach to improve the representation of Markov-CA transition rules. Utilised data relate to the environmental and socio-economic are produce criteria layers and weights in order to output the suitability maps via a ranking method for periods 1984, 1990, 2000 and 2010, which have become conditional for the next step of Markov-CA generation. The suitability maps are compared in order to determine the best fit maps based on the values of the root equation (R 2 ). This comparison helpful to the stakeholders make better decisions and the best maps indicated best transition rules, which is represent the core of Markov-CA model. Thus, the resultant of best suitability maps derives a predefined transition rule for the end of Markov-CA model step. The approach used in the present study a mechanism for monitoring the land-use and land-cover changes in Kirkuk depend on Markov-CA is implemented and evaluated thru the different decision maker. The results assert that the model bears a high applicability and flexibility degree in land-use and land-cover changes.

Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model

“…In statistical terms, the Markov chain modelling can effectively make a prediction of the changes in LULC based on the calculation of the transition probabilities of one system at time t 2 with the state of the system at time t 1 according to the specific year [ 41 , 87 ]. The transition probability matrix [ 88 ] is one of the descriptive tools generated in the process where the mangrove areas transitional matrix is derived from different mangrove classes [ 86 ]. The Markov processes used in this study are expressed in Eqs ( 7 – 9 ):…”

Multi-Spatiotemporal Analysis of Changes in Mangrove Forests in Palawan, Philippines: Predicting Future Trends Using Support Vector Machine Algorithm and Markov Chain Model

“…In statistical terms, the Markov Chain Modelling can effectively make a prediction of the changes in LULC based on the calculation of the transition probabilities of one system at time t2 with the state of the system at time t1 according to the specific year [41,87]. The transition probability matrix [88] is one of the descriptive tools generated in the process where the mangrove areas transitional matrix derived from different mangrove classes [86]. The Markov processes used in this study are expressed in equations (Eq 7), (Eq 8), and (Eq 9):…”

Multi-Spatiotemporal Analysis of Changes in Mangrove Forests in Palawan, Philippines: Predicting Future Trends Using Support Vector Machine Algorithm and Markov Chain Model

“…In statistical terms, the Markov Chain Modelling can effectively make a prediction of the changes in LULC based on the calculation of the transition probabilities of one system at time t2 with the state of the system at time t1 according to the specific year . The transition probability matrix (Omar 2014) is one of the descriptive tools generated in the process where the mangrove areas transitional matrix derived from different mangrove classes (Mukhopadhyay et al 2015). The Markov processes used in this study are expressed in equations (Eq 7), (Eq 8), and (Eq 9):…”