Document Type : Original Article


Shahid Beheshti University, Tehran, Iran


Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. The city of Tabriz has also experienced rapid growth of urban lands due to significant demographic changes, which requires accurate simulation of urban growth to prevent negative environmental and economic consequences. The purpose of this study was to evaluate the performance accuracy of the proposed machine learning algorithms by spatial cross-validation method in combination with the cellular automata model to simulate urban growth.
Material and methods: In this study, to analyze urban land-use changes, Landsat satellite images related to the years 1997, 2006, and 2015 were classified using the support vector machine algorithm. In the next step, change potential maps of non-urban to urban areas were produced using random forest algorithms, support vector machine, and multilayer perceptron neural network for two periods of calibration (1997 and 2006) and validation (2006 and 2015) based on distance from the main roads, distance from the city center, distance from built-up areas, distance from the rivers and railways, as well as slope, elevation, and two-class (agricultural/barren) land use layer as effective factors in the growth of the city. Finally, using the cellular automata model, the growth simulation of Tabriz city based on land use and change potential maps obtained from machine learning algorithms for the mentioned periods was performed. To prevent over-fitting of algorithms to training samples and to obtain optimistic results, in the process of extracting optimal parameters of machine learning algorithms, the spatial cross-validation method was used to reduce the spatial correlation between training and test data.
Results and discussion: The results showed that the random forest algorithm with the area under the ROC curve of 0.9228 compared to the support vector machine and multilayer perceptron neural network algorithms with 0.8951 and 0.8726, respectively, had a better performance in estimating the change potential of non-urban to urban areas. Furthermore, in comparison with others, the random forest also clearly showed local variations in potential change. Finally, the growth of Tabriz city was simulated using the cellular automata model based on the obtained change potential maps. Comparison of the prediction map in the validation period with the current situation of urban areas in 2015 showed that the accuracy of an urban growth simulation model based on random forest with a Figure of Merit index of 0.3569 compared to models based on support vector machine and artificial neural network was more accurate in allocating non-urban to urban lands with 0.3496 and 0.3434, respectively.
Conclusion: As machine learning algorithms such as artificial neural networks, support vector machines, and random forest are capable of solving non-linear problems, using them is strongly recommended for urban growth simulation. Also, among the algorithms used in this research, the random forest algorithm based on ensemble learning has a higher advantage than the two-support vector machine and the artificial neural network algorithms.


Aburas, M.M., Ho, Y.M., Ramli, M.F. and Ash’aari, Z.H., 2017. Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation. 59, 65-78.
Ana, D., Nikolik, K. and Curfs. L., 2004. Probabilistic SVM outputs for pattern recognition using analytical geometry. Neurocomputing. 62, 293-303.
Asghari, A., 2015. Modeling Urban Development Using Cellular Automate and Ant Colony Optimization Algorithm Case Study: Tehran . MS.c. Thesis. Shahid Beheshti University, Thran, Iran.
Breiman, L., 2001. Random forests. Machine Learning, Springer. 45, 5-32.
Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research. 13, 281-305.
Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Casalicchio, G. and Jones, Z.M., 2016. mlr: Machine Learning in R. The Journal of Machine Learning Research. 17, 5938-5942.
Clarke, K.C., Hoppen, S. and Gaydos, L., 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design. 24, 247-261.
Duarte, E. and Wainer, J., 2017. Empirical comparison of cross-validation and internal metrics for tuning SVM hyperparameters. Pattern Recognition Letters. 88, 6-11.
Feng, Y., Liu, M., Chen, L. and Liu, Y., 2016. Simulation of dynamic urban growth with partial least squares regression-based cellular automata in a GIS environment. International Journal of Geo-Information. 5, 243.
Gislason, P.O., Benediktsson, J.R. and Sveinsson, J.A., 2006. Random forests for land cover classification. Pattern Recognition Letters. 27, 294-300.
Ghasemi Esfahan, A., 2013. Investigation of stochastic forest method to improve urban land cover classification using satellite images. MS.c. Thesis. Khaje Nasireddin Toosi University, Tehran, Iran.
He, J., Xia, L., Yao,Y., Ye, H. and Jinbao, Z., 2018. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science. 32, 1362-3087.
Hosseinali, F., Alesheikh, A.A. and Nourian, F., 2013. Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities. 31, 105-113.
Hutter, F., Hoos, H.H. and Leyton-Brown, K., 2011. Sequential model-based optimization for general algorithm con_guration. In Lecture Notes in Computer Science. 6683, 507-523.
Huang, B. and Boutros, C., 2016. The parameter sensitivity of random forests, BMC Bioinformatics. 17, 331.
James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning. Springer, New York, USA.
Jat, M.K., Choudhary, M. and Saxena, A., 2017. Application of geo-spatial techniques and cellular automata for modelling urban growth of a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Science. 20(2), 223-241.
Javadi, Y., 2008. Modeling Land cover changes using Cellular Automata in GIS environment. MS.c. Thesis. University of Tehran, Tehran, Iran.
Kamusoko, C. and Gamba, J., 2015. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS International Journal of Geo-Information. 4, 447- 470.
Kiavarz Moghaddam, H. and Samadzadegan, F., 2009. Urban simulation using neural networks and cellular automata for land use planning. In: M. Schrenk, et al., eds. Proceeding of REAL CORP, Tagungsband, pp. 571–577.
Li, X. and Gong, P., 2016. Urban growth models: Progress and perspective. Science Bulletin. 61, 1637-1650.
Liu, X. Li, X. and Shi, X., 2008. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling. 211, 169-181.
Liu, X. Li, X. and Chen, Y., 2010. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology. 25, 671- 682.
Liu, Y. Feng, Y. and Pontius, R., 2014. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land. 3, 719-738.
Liang, X. Liu, X. Li, D. Zhao, H. and Chen, G., 2018. Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. International Journal of Geographical Information Science. 32, 2294-2316.
Lovelace, R. Nowosad, J. and Muenchow, J., 2019. Geocomputation with R: Statistical learning. CRC Press.
Mundia, C. N. and Aniya, M., 2007. Modeling urban growth of Nairobi city using cellular automata and geographical information systems. Geographical Review of Japan. 80, 777-788.
Maleki D., 2010. Modeling Urban Development Using Cellular Automation Method. M.Sc. Thesis. Khaje Nasireddin Toosi University of Technology, Tehran, Iran.
Mirbagheri, B. and Alimohammadi, A., 2018. Integration of local and global support vector machines to improve urban growth modelling. ISPRS International Journal of Geo-Information. 7, 347.
Moosavi, M., 2011. An introduction to environmental challenges of life in slum settlements of Tabriz. 2 th International Conference on Humanities. Historical and Social Sciences, 26 th -28 th February, Singapore. P.17.
Mustafa, A., Rienow, A., Saadi, I., Cools, M. and Teller, J., 2018. Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing.51, 391- 401.
Platt, J. C., 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 68, 61–74.
Pijanowski, B. C., Brown, D. G., Shellitoc, B. A. and Manikd, G. A., 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers. Environment and urban systems. 6, 553-575.
Pontius, G. R. and Malanson, J., 2005. Comparison of the Structure and Accuracy of Two Land Change Models. International Journal of Geographical Information Science. 19, 243-265.
Pontius G. R., Walker, R., Kumah, R., Arima, E., Aldrich, S., Caldad, M. and Vergara, D., 2007. Accuracy assessment for a simulation model of Amazonian deforestation. Annals of the American Association of Geographers. 97, 677-695.
Qian, Y., Xing, W., Guan, X., Yang, T. and Wu, H., 2020. Coupling cellular automata with area partitioning and spatiotemporal. Science of the Total Environment.722, 137738.
Rumelhart, D., Hinton,G. and Williams, R.,1986.. Learning representations by back-propagating errors. Nature. 323, 533-536.
Shafizadeh-Moghadam, H., Asghari, A., Tayyebi, A. and Taleai, M., 2017. Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Computers. Environment and Urban Systems. 64, 297-308.
Tobler, W. R., 1970. A computer movie simulating urban growth in the Detroit region. Economic geography. 46, 234-240.
UNFPA, 2016. State of world population 2016. New York, United Nations Population Fund.
Widodo, A., Yang, B.S. and Han, T., 2007. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert systems with applications. 32, 299-312.
White, R. and Engelen, G., 1997. Cellular automata as the basis of integrated dynamic regional modeling, Environment and Planning B: Planning & Design. 24, 235- 246.
Wu, F. and Webster, C. J., 1998. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B: Urban Analytics and City Science. 25, 103-126.
Wu, F. and Martin, D., 2002. Urban expansion simulation of Southeast England using population surface modelling and cellular automata. Environment and Planning. 34, 1855-1876.
Wu, T. F., Lin, C. J. and Weng, R. C., 2004. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research. 5, 975-1005.
Yang, Q., Li, X. and Shi, X., 2008. Cellular automata for simulating land use changes based on support vector machines. Computers & Geosciences. 34, 592-602.
Yao, Y., Li, X., Liu, X., Liu, P. and Liang, Z., 2017. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science. 31, 825-848.
Yeh, A.G., Li, X. and Xia, C., 2021. Cellular automata modeling for urban and regional planning. Urban Informatics. 45,865-883.
Zhang, Y., Liu, X., Chen, G. and Hu, G., 2020. Simulation of urban expansion based on cellular automata and maximum entropy model. Science China Earth Sciences. 63, 701-712.