Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated.

城市增长是一种动态的人口统计学现象，是指由于社会和经济力量而导致的城市地区（如城镇）空间价值的增加。如今，城市土地正在迅速增加，取代了非城市土地，例如农业，森林，水，农村和开放土地。在这项研究中，CA-Markov模型用于预测马来西亚芙蓉市城市土地的增长及其空间趋势。还评估了CA-Markov模型的性能。马尔可夫链模型用于产生城市和非城市土地的过渡概率的定量值。随后，CA模型被用来预测土地变化的动态空间趋势。使用CA-Markov模型对1984年至2010年之间城市和非城市土地利用的变化进行了建模，以进行校准并计算最佳的CA过渡规则，并预测未来的城市增长。为了进行准确性评估，使用kappa系数验证了CA-Markov模型。kappa指数统计的整体准确性为83％，这表明所提出模型的出色性能。最后，基于CA转换规则和基于马尔可夫链模型的校准过程产生的转换面积矩阵，对芙蓉市2020年和2030年的未来城市增长进行了模拟。这表明了所提出模型的出色性能。最后，基于CA转换规则和基于马尔可夫链模型的校准过程产生的转换面积矩阵，对芙蓉市2020年和2030年的未来城市增长进行了模拟。这表明了所提出模型的出色性能。最后，基于CA转换规则和基于马尔可夫链模型的校准过程产生的转换面积矩阵，对芙蓉市2020年和2030年的未来城市增长进行了模拟。