OPTIMUM PREDICTIVE MODEL FOR URBAN GROWTH PREDICTION
Keywords:
Urban growth prediction, CA-Markov model, logistic regression model, Nakhon Ratchasima provinceAbstract
This study aims to establish a framework to identify an optimum predictive model for simulation of urban dynamics in Nakhon Ratchasima province, Thailand. This study focuses on 2 different stochastic algorithms including the Cellular Automata Markov and logistic regression models. The core input data from interpreted land use in 1986 and 1994 were used to predict land use in 2002.The results are then compared with interpreted land use in 2002 to identify an optimum predictive model for urban growth. Results showed that the CA-Markov model provided higher overall accuracy and kappa hat coefficient of agreement for urban growth prediction in 2002 than the logistic regression model. Therefore, the CA-Markov model was chosen as an optimum predictive model for urban growth in 2010 and 2018. It was found that the urban and built-up area increased by about 36.32 sq. km (4.8% of the study area) between 2002 and 2010 while it increased by about70.87 sq. km (9.4 % of the study area) between 2002 and 2018.
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