Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth

Sustainable urban development is a focus of regional policy makers; therefore, how to measure and understand urban growth is an important research topic. This paper quantified the amount of urban growth on land use maps that were derived from multi-temporal Landsat images of Jiaxing City as a rapidly-growing city in Zhejiang Province from 2000–2015. Furthermore, a new approach coupled the heuristic bat algorithm (BA) and deep belief network (DBN) with the cellular automata (CA) model (DBN-CA), which was developed to simulate the urban expansion in 2015 and forecast the distribution of urban areas of Jiaxing City in 2024. The BA was proposed to obtain the best structure of the DBN, while the optimized DBN model considered the nonlinear spatial-temporal relationship of driving forces in urban expansion. Comparisons between the DBN-CA and the conventional artificial neural network-based CA (ANN-CA) model were also performed. This study demonstrates that the proposed model is more stable and accurate than the ANN-CA model, since the minimum and maximum values of the kappa coefficient of the DBN-CA were 77.109% and 78.366%, while the ANN-CA’s values were 63.460% and 76.151% over the 200 experiments, respectively. Therefore, the DBN-CA model is a potentially effective new approach to survey land use change and urban expansion and allows sustainability research to study the health of urban growth trends.

[1]  Min Cao,et al.  A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion , 2016, Int. J. Geogr. Inf. Sci..

[2]  Lina Tang,et al.  A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence , 2014, Int. J. Geogr. Inf. Sci..

[3]  Célia Ghedini Ralha,et al.  A multi-agent model system for land-use change simulation , 2013, Environ. Model. Softw..

[4]  Min Cao,et al.  A new discovery of transition rules for cellular automata by using cuckoo search algorithm , 2015, Int. J. Geogr. Inf. Sci..

[5]  Geoffrey E. Hinton.  Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[6]  F. Wu,et al.  Simulation of Land Development through the Integration of Cellular Automata and Multicriteria Evaluation , 1998 .

[7]  Robert V. O'Neill,et al.  CAUSES AND EFFECTS OF LAND-USE CHANGE IN CENTRAL RONDONIA, BRAZIL , 1993 .

[8]  Manchun Li,et al.  Calibrating a cellular automata model for understanding rural–urban land conversion: a Pareto front-based multi-objective optimization approach , 2014, Int. J. Geogr. Inf. Sci..

[9]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Xiaoping Liu,et al.  Calibrating cellular automata based on landscape metrics by using genetic algorithms , 2013, Int. J. Geogr. Inf. Sci..

[11]  Francesco Riccioli,et al.  Artificial neural network for multifunctional areas , 2015, Environmental Monitoring and Assessment.

[12]  J. Schwartz,et al.  Theory of Self-Reproducing Automata , 1967 .

[13]  Huaxiong Li,et al.  The Appropriate Hidden Layers of Deep Belief Networks for Speech Recognition , 2015, 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[14]  Yueqing Xu,et al.  An econometric analysis of changes in arable land utilization using multinomial logit model in Pinggu district, Beijing, China. , 2013, Journal of environmental management.

[15]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[16]  Luca Salvati,et al.  A spatial zoning approach to calibrate and validate urban growth models , 2017, Int. J. Geogr. Inf. Sci..

[17]  Michael R. Muller,et al.  A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada , 1994, Landscape Ecology.

[18]  Anthony S. Clark.  The Political Institutional Determinants Of Land-Use Change And Sprawl: A Conceptual Model , 2010 .

[19]  Shen Furao,et al.  Forecasting exchange rate using deep belief networks and conjugate gradient method , 2015, Neurocomputing.

[20]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[21]  Muhammad Ahsan Mahboob,et al.  Remote Sensing and GIS Applications for Assessment of Urban Sprawl in Karachi, Pakistan , 2015 .

[22]  Elisabete A. Silva,et al.  Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese metropolitan areas , 2005 .

[23]  Lin Liu,et al.  A bottom‐up approach to discover transition rules of cellular automata using ant intelligence , 2008, Int. J. Geogr. Inf. Sci..

[24]  Roberto Fratini,et al.  Use of cellular automata in the study of variables involved in land use changes , 2013, Environmental Monitoring and Assessment.

[25]  Hichem Omrani,et al.  Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale , 2014 .

[26]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[27]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[28]  Andreas Rienow,et al.  Supporting SLEUTH - Enhancing a cellular automaton with support vector machines for urban growth modeling , 2015, Comput. Environ. Urban Syst..

[29]  Demetris Stathakis,et al.  Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks , 2015 .

[30]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..

[31]  Y. Wei,et al.  Urban Land Expansion and Spatial Dynamics in Globalizing Shanghai , 2014 .

[32]  Anette Reenberg,et al.  A system dynamics approach to land use changes in agro-pastoral systems on the desert margins of Sahel , 2012 .

[33]  Mohsen Ahadnejad Reveshty,et al.  Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran , 2016 .

[34]  Biswajeet Pradhan,et al.  Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model , 2014, Journal of the Indian Society of Remote Sensing.

[35]  Chen Zeng,et al.  A partitioned and asynchronous cellular automata model for urban growth simulation , 2016, Int. J. Geogr. Inf. Sci..

[36]  Keith C. Clarke,et al.  Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore , 1998, Int. J. Geogr. Inf. Sci..

[37]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[38]  Inés Santé-Riveira,et al.  Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain , 2013, Int. J. Geogr. Inf. Sci..

[39]  Lien Poelmans,et al.  Complexity and performance of urban expansion models , 2010, Comput. Environ. Urban Syst..

[40]  Mark Brussel,et al.  Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India , 2014 .

[41]  Keith C. Clarke,et al.  A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area , 1997 .

[42]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[43]  Jinyu Li,et al.  Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks. , 2013, ICLR 2013.

[44]  Fulong Wu,et al.  SimLand: A Prototype to Simulate Land Conversion Through the Integrated GIS and CA with AHP-Derived Transition Rules , 1998, Int. J. Geogr. Inf. Sci..

[45]  Michael Batty,et al.  From Cells to Cities , 1994 .

[46]  George Grekousis,et al.  Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area , 2013 .

[47]  Bryan C. Pijanowski,et al.  An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran , 2011 .

[48]  Hongbin Wang,et al.  A new temporal–spatial dynamics method of simulating land-use change , 2017 .

[49]  M. Nagai,et al.  Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models , 2016 .

[50]  Emiliano Ferreira Castejon,et al.  Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics , 2008, Int. J. Geogr. Inf. Sci..

[51]  Keith C. Clarke,et al.  Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach , 2016 .

[52]  Roger White,et al.  Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns , 1993 .