Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory

Ahmad, F., Hamdan, A. B., & Bakar, A. A. (2007). Mining for interesting indicators using reduction approach. In Proceedings of the international conference on electrical engineering and informatics. Indonesia: Institut Teknologi Bandung (June 17–19). Al-Ahmadi, 2009, Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia, Ecological Complexity, 6, 80, 10.1016/j.ecocom.2008.09.004 Batty, 2005 Benenson, 2004, Geosimulation: Object-based modeling of urban phenomena, Computers, Environment and Urban Systems, 28, 1, 10.1016/S0198-9715(02)00067-4 Brown, 2005, Path dependence and the validation of agent-based models of land use, International Journal of Geographical Information Science, 19, 153, 10.1080/13658810410001713399 Clarke, 2008, Mapping and modelling land use change: An application of the SLEUTH model, 353 Clarke, 1994, A cellular automaton model of wildfire propagation and extinction, Photogrammetric Engineering and Remote Sensing, 60, 1355 Clarke, 1998, Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore, International Journal of Geographical Information Science, 12, 699, 10.1080/136588198241617 ESRI, 2006 Fang, 2005, The impact of interactions in spatial simulation of the dynamics of urban sprawl, Landscape and Urban Planning, 73, 294, 10.1016/j.landurbplan.2004.08.006 Hagen, A. (2002). Multi-method assessment of map similarity. In The 5th AGILE conference on geographic information science, Palma, Spain (April). Hagoort, 2008, Spatial externalities, neighbourhood rules and CA land-use modelling, The Annals of Regional Science, 42, 39, 10.1007/s00168-007-0140-8 Han, 2001, Supervised learning: A generalized rough set approach, 322 Hasbani, J. -G. (2008). Semi-automated calibration of a cellular automata model to simulate land-use changes in the Calgary region. Unpublished M.Sc. thesis, Department of Geomatics Engineering, University of Calgary, p. 96. Hassen, 2003, Adaptive behavior in cellular automata using rough set theory, Applied Artificial Intelligence, 17, 155, 10.1080/713827104 Kocabas, 2006, Assessing cellular automata model behaviour using a sensitivity analysis approach, Computers, Environment and Urban Systems, 30, 921, 10.1016/j.compenvurbsys.2006.01.001 Lau, 2005, A cellular automata model for urban land-use simulation, Environment and Planning B, 32, 247, 10.1068/b31110 Li, 2001, Calibration of cellular automata by using neural networks for the simulation of complex urban systems, Environment and Planning A, 33, 1445, 10.1068/a33210 Li, 2002, Integration of principal components analysis and cellular automata for spatial decision making and urban simulation, Science in China, 45, 521, 10.1360/02yd9054 Li, 2004, Data mining of cellular automata’s transition rules, International Journal of Geographical Information Science, 18, 723, 10.1080/13658810410001705325 Liu, 2003, Modeling urban development with cellular automata incorporating fuzzy-set approaches, Computers, Environment and Urban Systems, 27, 637, 10.1016/S0198-9715(02)00069-8 Ménard, 2007, Simulating the impact of forest management scenarios in an agricultural landscape of Southern Quebec, Canada, using a geographic cellular automata, Landscape and Urban Planning, 79, 253, 10.1016/j.landurbplan.2006.02.016 Moreno, 2009, Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model, Computers, Environment and Urban Systems, 33, 44, 10.1016/j.compenvurbsys.2008.09.008 Nguyen, H. S. (1998). Discretization problem for Rough Sets Methods. In L. Polkowski & A. Skowron (Eds.), Rough sets and current trend in computing (pp. 545–552). Proceedings of the first international conference, RSCTC’98, Warsaw, Poland (June). Øhrn, A. (1999). Discernibility and rough sets in medicine: Tools and applications. Ph.D. thesis, Department of Computer and Information Science, Norwegian University of Science and Technology. Øhrn, A., Komorowski, J., Skowron, A., & Synak, P. (1998). The design and implementation of a knowledge discovery toolkit based on rough sets: The Rosetta system. In L. Polkowski & A. Skowron (Eds.), Rough sets in knowledge discovery 1: Methodology and applications, studies in fuzziness and soft computing (pp. 376–399). Physica-Verlag. Pan, 2010, The impact of variation in scale on the behavior of a cellular automata used for land-use change modeling, Computers, Environment and Urban System, 34, 400, 10.1016/j.compenvurbsys.2010.03.003 Pawlak, 1991 Pawlak, 1998, Rough set theory and its applications to data analysis, Cybernetics and Systems: An International Journal, 29, 661, 10.1080/019697298125470 Questier, 2002, Application of rough set theory to feature selection for unsupervised clustering, Chemometrics and Intelligent Laboratory Systems, 63, 155, 10.1016/S0169-7439(02)00041-2 RIKS BV (2010). <http://www.riks.nl/mck>. Rosetta development team (2009). <http://www.lcb.uu.se/tools/rosetta>. Sirakoulis, 2000, A cellular automaton model for the effects of population movement and vaccination on epidemic propagation, Ecological Modelling, 133, 209, 10.1016/S0304-3800(00)00294-5 Sui, 2001, Modeling the dynamics of landscape structure in Asia’s emerging desakota regions: A case study in Shenzhen, Landscape and Urban Planning, 53, 37, 10.1016/S0169-2046(00)00136-5 Swiniarski, 2003, Rough set methods in feature selection and recognition, Pattern Recognition Letters, 24, 833, 10.1016/S0167-8655(02)00196-4 Tobler, 1979, Cellular geography, 379 Van Vliet, 2009, Modeling urban growth using a variable grid cellular automata, Computers, Environment and Urban Systems, 33, 35, 10.1016/j.compenvurbsys.2008.06.006 Verburg, 2004, A method to analyse neighbourhood characteristics of land use patterns, Computers, Environment and Urban Systems, 28, 667, 10.1016/j.compenvurbsys.2003.07.001 Vinterbo, 2000, Minimal approximate hitting sets and rule templates, International Journal of Approximate Reasoning, 25, 123, 10.1016/S0888-613X(00)00051-7 Von Neumann, 1963, General and logical theory of automata, 288 Wang, 2005, On acquiring classification knowledge from noisy data based on rough set, Expert Systems with Applications, 29, 49, 10.1016/j.eswa.2005.01.005 White, 2006, Pattern based map comparisons, Journal of Geographical Systems, 8, 145, 10.1007/s10109-006-0026-9 White, 2000, High-resolution integrated modeling of the spatial dynamics of urban and regional systems, Computer, Environment and Urban Systems, 24, 383, 10.1016/S0198-9715(00)00012-0 White, 1997, The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics, Environment and Planning B: Planning and Design, 24, 323, 10.1068/b240323 Wolfram, 1984, Computation theory of cellular automata, Communications in Mathematical Physics, 96, 15, 10.1007/BF01217347 Wu, 1998, An experiment on the generic polycentricity of urban growth in a cellular automatic city, Environment and Planning B, 25, 731, 10.1068/b250731 Wu, 2002, An experiment on the generic polycentricity of urban growth in a cellular automatic city, Environment and Planning B, 25, 731, 10.1068/b250731 Wu, 2009, Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk, Ecological Modelling, 220, 3490, 10.1016/j.ecolmodel.2009.03.008 Yang, 2006, Mining transition rules for geo-simulation using rough sets, Acta Geographica Sinica, 61, 882 Yassemi, 2008, Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour, Ecological Modelling, 210, 71, 10.1016/j.ecolmodel.2007.07.020