Benefits of a multiple-solution approach in land change models

Land change (LC) science seeks to understand the dynamics of land cover and land use change (Turner, Lambin, & Reenberg, 2007). However, land cover is a distinct concept from land use. Land cover is the physical material that covers a specific land of the Earth, whereas land use shows how people use the land (Fisher, Comber, & Wadsworth, 2005). For example, the grass is a land cover which can be found in many land uses such as urban parks and pastures. Received: 20 March 2018 | Revised: 20 July 2018 | Accepted: 12 August 2018 DOI: 10.1111/tgis.12482

[1]  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 .

[2]  Alan T. Murray,et al.  A stochastically constrained cellular model of urban growth , 2000 .

[3]  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..

[4]  Wei Tang,et al.  A land-use spatial optimization model based on genetic optimization and game theory , 2015, Comput. Environ. Urban Syst..

[5]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[6]  Linda See,et al.  Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia , 2009 .

[7]  A. Achmad,et al.  Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh, Indonesia , 2015 .

[8]  Mario Cools,et al.  Comparing support vector machines with logistic regression for calibrating cellular automata land use change models , 2018 .

[9]  Mario Cools,et al.  Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm , 2018, Comput. Environ. Urban Syst..

[10]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[11]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[12]  Xiaodong Yin,et al.  Investigations On Solving the Load Flow Problem By Genetic Algorithms , 1991 .

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

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

[15]  Tung-Kuan Liu,et al.  A Novel Crowding Genetic Algorithm and Its Applications to Manufacturing Robots , 2014, IEEE Transactions on Industrial Informatics.

[16]  Ole J Mengshoel,et al.  The Crowding Approach to Niching in Genetic Algorithms , 2008, Evolutionary Computation.

[17]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[18]  Mario Cools,et al.  Modelling Uncertainties in Long-Term Predictions of Urban Growth: A Coupled Cellular Automata and Agent-Based Approach , 2015 .

[19]  Shigenobu Kobayashi,et al.  Adaptive isolation model using data clustering for multimodal function optimization , 2005, GECCO '05.

[20]  Bryan C. Pijanowski,et al.  A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment , 2014, Environ. Model. Softw..

[21]  Daniel G. Brown,et al.  A review of current calibration and validation practices in land-change modeling , 2016, Environ. Model. Softw..

[22]  Matthew Hall,et al.  A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization , 2013, ArXiv.

[23]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[24]  R. Fillingim,et al.  Geospatial analysis of Hospital Consumer Assessment of Healthcare Providers and Systems pain management experience scores in U.S. hospitals , 2014, PAIN®.

[25]  R. White,et al.  High-resolution integrated modelling of the spatial dynamics of urban and regional systems , 2000 .

[26]  Weiguo Sheng,et al.  Adaptive Multisubpopulation Competition and Multiniche Crowding-Based Memetic Algorithm for Automatic Data Clustering , 2016, IEEE Transactions on Evolutionary Computation.

[27]  Kalyanmoy Deb,et al.  Comparison of multi-modal optimization algorithms based on evolutionary algorithms , 2006, GECCO.

[28]  Xiaodong Yin,et al.  A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization , 1993 .

[29]  Yan Liu,et al.  Modeling dynamic urban growth using cellular automata and particle swarm optimization rules , 2011 .

[30]  A. S. Mahiny,et al.  Guiding SLEUTH Land-Use/Land-Cover Change Modeling Using Multicriteria Evaluation: Towards Dynamic Sustainable Land-Use Planning , 2012 .

[31]  Xiaodong Li,et al.  Species based evolutionary algorithms for multimodal optimization: A brief review , 2010, IEEE Congress on Evolutionary Computation.

[32]  Xin Yang,et al.  A land use change model: Integrating landscape pattern indexes and Markov-CA , 2014 .

[33]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[34]  Samir W. Mahfoud.  Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization , 1993, ICGA.

[35]  Suzana Dragicevic,et al.  Modeling Urban Land Use Changes Using Support Vector Machines , 2016, Trans. GIS.

[36]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[37]  Mario Cools,et al.  Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM) , 2017 .