Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and dynamic problems in urban studies. The goal of this article is a review of current literature in the field of planning and AI. The aim of this review is to increase the understanding of how AI approaches urban and land dynamics modeling processes and how, as a result, researchers can structure that knowledge and choose the correct approaches to embed in their models. For this purpose, the authors review the applications of AI techniques in urban land dynamics domain as well as the emerging challenges they face. The authors discuss hybrid AI systems as a need resulting from the trend in planning policy to develop more holistic approaches. The authors conclude that, although challenges exist, AI-based approaches offer promising solutions for urban and land dynamics.
Abbaspour, K.C., R. Schulin, and M. T. van Genuchten .2001. Estimating unsaturated soil hydraulic parameters using ant colony optimization. Advance in Water Resources24:827-41.
2.
Abbott, M.B.1989. M.B. Abbott, Review of recent developments in coastal modelling. In: R.A. Falconer.
3.
Abdullah, I. Al-Odienat, and Ayman A. Al-Lawama .2008. The advantages of PID fuzzy controllers over the conventional types. American Journal of Applied Sciences5 (6): 653-58.
4.
Aerts, J. C. J. H, and G.B.M. Heuvelink .2002. Using simulated annealing for resource allocation. International Journal of Geographical information Science16:571-87.
5.
Ahmad, S., and S.P. Simonovic.2001. Integration of heuristic knowledge with analytical tools for the selection of flood damage reduction measures. Canadian Journal of Civil Engineering28 (2): 208-21.
6.
Aiazzi, B., L. Alparone, and S. Baronti.2003. Land cover classification of urban and sub-urban areas via fuzzy nearest-mean reclustering of SAR features. 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 62-66.
7.
Al-Hassan, W., M.B. Fayek, and S.I. Shaheen.2006. PSOSA: An optimized particle swarm technique for solving the urban planning problem. 2006 International Conference on Computer Engineeing & Systems, 401-5.
8.
Al-Kheder, S., Jun Wang, and Jie Shan.2007. Cellular automata urban growth model calibration with genetic algorithms. Urban Remote Sensing Joint Event11-13:1-5.
9.
Al-kheder, S., Wang, J. and Shan, J.2008. Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images. International Journal of Geographical Information Science, 22, 1271-1293.
10.
Álvaro, Castro Castilla , and Nuria Gómez Blas.2008. Self-organizing map and cellular automata combined technique for advanced mesh generation in urban and architectural design. International Journal of Information Technologies and Knowledge2:354-60.
11.
Anna, Awrynowicz.2007. Hybrid approach with an expert system and a genetic algorithm to production management in the supply net. Intelligent Systems in Accounting, Finance & Management14 (1-2): 59-76.
12.
Annelie, Holzkämper, and Ralf Seppelt.2007. A generic tool for optimising land-use patterns and landscape structures. Environmental Modelling & Software22 (12): 1801-4.
13.
Arentze, T.A., A.W.J. Borgers, and H.J.P. Timmermans .2006. ‘‘A heuristic method for land-use plan generation in planning support systems’’ , in: J. P. van Leeuwen and H. J. P. Timmermans (eds.) Progress in Design & Decision Support Systems in Architecture and Urban Planning, Eindhoven, The Netherlands, Springer, Dordrecht , p. 135-152.
14.
Arijit, Laha, Nikhil R. Pal, and Jyotirmoy Das .2006. Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory. IEEE Transactions of Geoscience and Remote Sensing44 (6):1633-1642.
15.
Atkinson, Peter, Giles Foody, Steve Darby, and Fulong Wu.2005. GeoDynamics. London: CRC Press.
16.
Balling Richard, Brent Powell, and Mitsuro Saito.2004. Generating future land-use and transportation plans for high-growth cities using a genetic algorithm. Computer-Aided Civil and Infrastructure Engineering19 (3): 213-22.
17.
Balling, R.J., J.T. Taber, M.R. Brown, et al. 1999. Multiobjective urban planning using genetic algorithm. Journal of Urban Planning and Development-ASCE125 (2): 86-99.
18.
Balmann, A., K. Happe, K. Kellermann, and A. Kleingarn.2002. Adjustment costs of agri-environment policy switchings: An agent-based analysis of the German region Hohenlohe . In Complexity and ecosystem management: The theory and practice of multi-agent systems, ed. M. Janssen, 127-57. Cheltenham , UK: Edward Elgar.
19.
Barredo, Jose I., K. Marjo, et al. 2003. Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning64:45-160.
20.
Batty, M.2004. Editorial. Environment and Urban Planning B31:326-30.
21.
Batty, M.2005. Agents, cells, and cities: New representational models for simulating multiscale urban dynamics. Environment and Planning A37 (8): 1373-94.
22.
Batty, M.2007. Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. Journal of Regional Science47 (3): 624-27.
23.
Batty, M., and P. Longley.1994. Fractal cities. A geometry of form and function. London: Academic Press .
24.
Batty, M., and Y. Xie.1996. Preliminary evidence for a theory of the fractal city. Environment and Planning A28:1745-62.
25.
Batty, M., Y. Xie, and Z. Sun.1999. Modelling urban dynamics through GIS-based CA. In Computers, environment and urban systems. New York: Elsevier.
26.
Benenson, I., and P.M. Torrens.2003. Geographic automata systems: A new paradigm for integrating GIS and geographic simulations. Presented at Geocomputation Conference, Southampton, UK .
27.
Beni, G., and J. Wang.1989. Swarm intelligence in cellular robotic systems. Presented at NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26-30.
28.
Berger, T.2001. Agent-based spatial models applied to agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agriculture Economics25:245-60.
29.
Bethell, T.2006. The search for artificial intelligence. American Spectator39 (6): 26-35.
30.
Bishop, J.M.1989. Stochastic searching networks. In Proceeding of the 1st IEE Conference on Artificial Neural Networks, 329-31. London.
31.
Bolliger, Janine.2005. Simulating complex landscapes with a generic model: Sensitivity to qualitative and quantitative classifications. Ecological Complexity2 (2): 131-49.
32.
Brown, Daniel G., and Derek T. Robinson .2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society11.
33.
Brown, I.2006. Modelling future landscape change on coastal floodplains using a rule-based GIS. Environmental Modelling & Software21:1479-90.
34.
Caglioni, M., M. Pelizzoni, and G.A. Rabino.2006. Urban sprawl: A case study for project gigalopolis using SLEUTH model. Lecture Notes in Computer Science, 4173: 436-45.
35.
Candau, J.Steen, Rasmussen, Keith C. Clarke.2000. Coupled cellular automaton model for land use/land cover dynamics, 4th International Conference on Integrating GIS and Environmental Modeling(GIS/EM4):Problems, Prospects and Research Needs. Banff, Alberta, Canada, September 2-8, 2000.
36.
Cantarella, G.E., G. Pavone, and A. Vitetta.2006. Heuristics for urban road network design: Lane layout and signal settings. European Journal of Operational Research175 (3): 1682-95.
37.
Chaker W., B. Moulin, and M. Thriault.2007. Modelling transport networks with design pattern: Application to hybrid traffic simulations. Presented at 18th IASTED International Conference Modelling and Simulation , Quebec, Canada.
38.
Chau, K.W.2006a. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol329:363-67.
39.
Chau, K.W.2006b. A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin52 (7): 726-33.
40.
Chau, K.W. and Chen, W.2001. A fifth generation numerical modelling system in coastal zone, Applied Mathematical Modelling25 (10): 887-900.
41.
Chen, M., and A.S. Alfa.1991. A network design algorithm using a stochastic incremental traffic assignment approach. Transportation Science25 (3): 215-24.
42.
Chen, Serena H., Anthony J. Jakeman, and John P. Norton .2008. Artificial intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and Computers in Simulation78 (2-3): 379-400.
43.
Cheng, J.2003. Modelling spatial & temporal urban growth. Doctoral diss., Department of Geographical Sciences, Utrecht University , the Netherlands.
44.
Choi, J.M., and E.L. Usery.2004. System integration of GIS and a rule-based expert system for urban mapping. Photogrammetric Engineering and Remote Sensing70 (2): 217-24.
45.
Claudia, Maria de Almeida, and Jose Marinaldo Gleriani.2005. Cellular automata and neural network as a modelling framework for the simulation of urban land use change. Presented at Anais XII Simpósio Brasileiro de Sensoriamento Remoto, Goiânia, Brasil, April 16-21.
46.
DeCastro, L.N., and J. Timmis.2002. Artificial immune systems: A new computational intelligence approach. London: Springer.
47.
Dhar, V., and R. Stein.1997. Intelligent decision support methods. The science of knowledge work. Englewood Cliffs, NJ: Prentice Hall.
48.
Dietzel, C., and K.C. Clarke.2004. Replication of spatio-temporal land use patterns at three levels of aggregation by an urban cellular automata . Lecture Notes in Computer Science3305:523-32.
49.
Dijkstra, J., J. Jessurun, and H.J.P. Timmermans .2002. A multi-agent cellular automata model of pedestrian movement. In Pedestrian and evacuation dynamics, ed. M. Schreckenberg and S. D. Sharma, 173-80. Berlin: Springer.
50.
Dimitriou, Loukas, Tsekeris, Theodore, Stathopoulos, Antony.2008. Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow, Transportation Research Part C: Emerging Technologies, 16(5): pp. 554-573.
51.
Dorigo, M., and C. Blum.2005. Ant colony optimization theory: A survey . Theoretical Computer Science344:243-78.
52.
Dragicevic, S.2004. Coupling fuzzy sets theory and GIS-based cellular automata for land-use change modelling. In Fuzzy Information, IEEE Annual Meeting of the Processing NAFIPS’04, Banff, Canada, 27-30 June 1:203-7.
53.
Dragicevic, S.2007. Embedding spatial agents into irregular cellular automata models of urban land use change to improve scenario exploration and decision making. Presented at GeoComputation’07 Conference , Maynooth, Ireland, September 3-5.
54.
Ducca, F.W., and R.H. Wilson.1976. A model of shopping center location . Environment and Planning A8 (6): 613-23.
55.
Duh, Jiunn-Der, and Daniel G. Brown .2007. Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation. Computers, Environment and Urban Systems31 (3): 253-81.
56.
Ebadi, Hamid, S. Roozbeh, M. Javad Valadanzoej, and V. Alireza.2004. Evaluation of indexing overlay, fuzzy logic and genetic algorithm methods for industrial estates site selection In GIS environment. International Society for Photogrammetry and Remote Sensing, Instanbul, Turkey.
57.
Eleftherios, A. Mandelas, Thomas Hatzichristos, and Poulicos Prastacos .2007. Fuzzy cellular automata based shell for modelling rrban growth-A pilot application in Mesogia area. Presented at 10th AGILE International Conference on Geographic Information Science, Aalborg University, Denmark.
58.
Ettema, D.F., K. De Jong, H.J.P. Timmermans, and A. Bakema.2007. PUMA: Multi-agent moddeling of urban systems. In Moddeling land-use change, ed. H. J. Scholten, E. Kommen , and J. Stilwell.Berlin: Springer Verlag.
59.
Fang-Chih Tien, Kuang-Han Hsieh, Chun-Yuan Cheng, and Chi-Shuan Liu .2007. Using hybrid genetic algorithm to solve discrete location allocation problems with rectilinear distance. Journal of the Chinese Institute of Industrial Engineers24 (1): 1-19.
60.
Fedra, K., L. Winkelbauer, and V.R. Pantulu.1991. Expert systems for environmental screening: An application in the lower mekong basin. International Institute for Applied System Analysis, Laxenburg.
61.
Feng, S., and L. Xu.1999. An intelligent decision support system for fuzzy comprehensive evaluation of urban development. Expert Systems with Applications16 (1): 21-32.
62.
Ferdinando, Semboloni.2006. Optimization and control of the urban spatial dynamics . Complexus 2004/20052:204-16.
63.
Filis, V., M. Sabrakos, C.P. Yialouris, A.B. Sideridis, and B. Mahaman.2003. GEDAS: An integrated geographical expert database system. Expert Systems with Applications24 (1): 25-34.
64.
Frans, Tillema.2003. Calibration of a conceptual LUTI model based on neural networks. Presented at 16th Mini-EURO Conference and 10th Meeting of EWGT.
65.
Garret, J.H.1994. Where and why artificial neural networks are applicable in civil engineering. Journal of Computing in Civil Engineering8 (2): 129-30.
66.
Geertman, S., M. Hagoort, and H. Ottens.2007. Spatial-temporal specific neighbourhood rules for cellular automata land-use modelling. International Journal of Geographical Information Science21 (5): 547-68.
67.
Gill, M.K., Y.H. Kaheil, A. Khalil, M. McKee, and L. Bastidas.2006. Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resour. Res. 42 (2006): W07417.
68.
Goldstein, N.C.2005. Brains versus brawn-Comparative strategies for the calibration of a cellular automata-based urban growth model. In GeoDynamics , ed. P. M. Atkinson, 249-72. Boca Raton, FL: CRC Press.
69.
Goodwins, R.2001. The machine that wanted to be a mind. ZDNet News Portal. http://news.zdnet.co.uk/story/0,,s2083911,00.html
70.
Guan, Qingfeng, Liming Wang, and Keith C. Clarke .2005. An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science32:369-80.
71.
Gustav Lundberg, C.1991. Knowledge-dependent models of spatial behavior. Gepgrafiska Annaler, Series B, huaman geography73:157-73.
72.
Happe, K.2004. Agricultural policies and farm structures: Agent-based modelling and application to EU policy reform. IAMO Studies on the Agricultural and Food Sector in Central and Eastern Europe, vol. 30. Halle, Germany: IAMO.
73.
He, Ruichun, Li Yinzhen, and Feng Xuesong.2007. Models and genetic algorithms for the optimal stochastic riding routes in urban public transportation and its applications . ICNC 2007: Third International Conference on Natural Computation5:431-35.
74.
Holland, John H.1975. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.
Hua, Jia, and Yohei Sato.2001. Land transformation research through the integrated CA GIS and RS with AHP-derived transition rules. http://www.-cger.nies.go.jp/lugec/lu-gec%20vi/23-ver5.pdf
77.
Hurley, S., and R.M. Whitaker.2002. An agent based approach to site selection for wireless networks. In Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain, ACM, 574-77.
78.
Iacono, M., D. Levinson, and A. El-Geneidy.2008. Models of transportation and land use change: A guide to the territory. Journal of Planning Literature22 (4): 323-40.
79.
Ilsoo, Yun Byungkyu.2006. Application of stochastic optimization method for an urban corridor. Proceedings of the 2006 Winter Simulation Conference1-5:1493-99.
80.
Inés, Santé-Riveira , Marcos Boullón-Magán, Rafael Crecente-Maseda, and David Miranda-Barrós.2008. Algorithm based on simulated annealing for land use allocation . Computers & Geosciences34 (3): 259-68.
81.
Itzhak, Benenson, Karel Martens, and Slava Birfir.2007. Agent-based model of driver parking behaviour as a tool for urban parking policy evaluation. Presented at 10th AGILE Conference on GIS Science, Aalborg , Denmark.
82.
Jenerette, G.D., and J.G. Wu.2001. Analysis and simulation of land-use change in the central Arizona-Phoenix region, USA. Landscape Ecology16 (7): 611-26.
83.
Jia, Lei, Licai Yang, Qingjie Kong, and Shu Lin .2006. Study of artificial immune clustering algorithm and its applications to urban traffic control. International Journal of Information Technology12 (3). Retrieved from http://www.icis.ntu.edu.sg/scs-ijit/1203/1203_1.pdf
84.
Jia, Tao, Li Xia, Xiaoping Liu, and Jinqiang He.2007. Analytical learning-based intelligent cellular automata for realistic and idealized urban simulation . Geography and Geo-Information Science23 (5): 43-47 (in Chinese).
85.
Jiao, J.F., and L. Boerboom.2006. Transition rule elicitation methods for urban cellular automata models. In Innovations in Design & Decision Support Systems in Architecture and Urban Planning, Springer, Dordrecht, 53-68.
86.
Jun, C.2000. Design of an intelligent geographic information system for multi-criteria site analysis. URISA Journal12 (3): 5-17.
87.
Kalapanidas, E., and N. Avouris.2001. Short-term air quality prediction using a case-based classifier. Environment Modelling and Software16:263-72.
88.
Kalogirou, S.2002. Expert systems and GIS: An application of land suitability evaluation. Computers, Environment and Urban Systems26 (2-3): 89-112.
89.
Kalogirou, S.A.2003. Artificial intelligence for the modeling and control of combustion processes: A review. Progress in Energy and Combustion Science29:515-66.
90.
Kawano, S., V.N. Huynh, M. Ryoke, and Y. Nakamori.2005. A context-dependent knowledge model for evaluation of regional environment. Environment Modelling and Software20:343-52.
91.
Kocabas, Verda, and Suzana Dragicevic .2007. Enhancing a GIS cellular automata model of land use change: Bayesian networks, influence diagrams and causality. Transactions in GIS11 (22): 681-702.
92.
Langton, C.1986. Studying artificial life with cellular automata. Physica D22:120-49.
93.
Lansing, J.S., and J.N. Kremer.1993. Emergent properties of Balinese water temple networks: Coadaptation on a rugged fitness landscape. Am Anthropol95:97-114.
94.
Lekkas, G.P., N.M. Avouris, and L.G. Viras.1994. Case-based reasoning in environmental monitoring applications. Applied Artificial Intelligence8:359-76.
95.
Li, X., and Xiaoping Liu .2007. Defining agents’ behaviours to simulate complex residential development using multicriteria evaluation . Journal of Environmental Management, 85(4):1063-75.
96.
Li, X., Q.S. Yang, et al. 2007. Genetic algorithms for determining the parameters of cellular automata in urban simulation. Science in China Series D-Earth Sciences50 (12): 1857-66.
97.
Li, X., Ye Jia’an, and Liao Qifang.2004. Case-based reasoning (CBR) for land use classification using radar images. Journal of Remote Sensing , 85(4):1063-1075.
98.
Li, X., and A.G.-O. Yeh .2001. Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A33 (8): 1445-62.
99.
Li, X., and A.G. Yeh.2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science16:323-43.
100.
Li, Yuanhai, and Amy B.Chan Hilton.2007. Optimal groundwater monitoring design using an ant colony optimization paradigm. Environmental Modelling & Software22 (1): 110-16.
101.
Liang, N., and Q. Gao.1999. ASSG-An expert system for sandy grasslands in North China. Expert Systems with Applications16:3-11.
102.
Ligmann, Zielinska A., and P. Jankowski.2007. Agent-based models as laboratories for spatially explicit planning policies. Environment and Planning B: Planning and Design34 (2): 316-35.
103.
Ligtenberg, A., et al. 2001. Multi-actor-based land use modelling: Spatial planning using agents. Land Use and Urban Planning56:21-33.
104.
Ligtenberg, A., M. Wachowicz, A.K. Bregt, A. Beulens, D.L. Kettenis.2004. A design and application of a multi-agent system for simulation of multi-actor spatial planning. Environmental Management72:43-55.
105.
Liu, X.H., A.K. Skidmore, and H.V. Oosten.2002. Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing56 (4): 257-68.
106.
Liu, XiaoPing, Li Xia, Anthony Gar-On Yeh, He JinQiang, and Tao Jia .2007. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D: Earth Sciences50:1578-88.
107.
Liu Yan, and R. Stuart.2003. Phinn modelling urban development with cellular automata incorporating fuzzy-set approaches. Computers, Environment and Urban Systems27 (6): 637-58.
108.
Liu, Zhi-Yong, and Ma Feng-We .2007. On-line reinforcement learning control for urban traffic signals. Proceedings of the 26th Chinese Control Conference6:34-37.
109.
Loibl, W., and T. Toetzer.2003. Modelling growth and densification processes in suburban regions-Simulation of landscape transition with spatial agents . Environmental Modelling & Software18 (6): 553-63.
110.
Los, M.1973. Spatial design and artificial intelligence. Philadelphia: University of Pennsylvania Press.
111.
Lu, S.F., and X.M. Liu.2007. Based on hybrid genetic algorithm and cellular automata combined traffic signal control and route guidance. Proceedings of the 26th Chinese Control Conference6:53-57.
112.
Luckman, P.G., M.R. Jessen, and R.G. Gibb.1990. Use of expert systems and GIS in land evaluation. New Zealand Geographer46:15-20.
113.
Lucic, P., and D. Teodorovic.2001. Bee system: Modeling combinatorial optimization transportation engineering problems by swarm intelligence. In Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis, Sao Miguel, Azores Islands, Portugal, June, 441-45.
114.
Madsen, H.2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology235 (3-4): 276-88.
115.
Mahesh, Pal.2008. Artificial immune-based supervised classifier for land-cover classification. International Journal of Remote Sensing29 (8): 2273-2291.
116.
Maier, H.R., A.C. Zecchin, L. Radbone, and P. Goonan.2006. Optimising the mutual information of ecological data clusters using evolutionary algorithms. Math. Comput. Model44:439-50.
117.
Malczewski, J.2004. GIS-based land-use suitability analysis: A critical overview . Progress in Planning62:3-65.
118.
Manson, S.M.2005. Agent-based modelling and genetic programming for modelling land change in the Southern Yucatan Peninsular Region of Mexico. Agriculture , Ecosystems & Environment111:47-62.
119.
Marc, Los, and Sang Nguyen.1981. Spatial allocation on a network with congestion. Transportation Research Part B: Methodological15 (2): 113-26.
120.
Marco, Dorigo, and Thomas Stützle .2004. Ant colony optimization. Cambridge, MA: MIT Press.
121.
Mathey, A.-H., E. Krcmar, S. Dragicevic, and I. Vertinsky.2008. Object-oriented cellular automata model for forest planning problems. Ecological Modelling212:359-71.
122.
Matott, L.S., A.J. Rabideau, and J.R. Craig.2006. Pump-and-treat optimization using analytic element method flow models. Advances in Water Resources29:760-75.
123.
Matsuba, Ikuo, and Masanori Namatame .2003. Scaling behaviour in urban development process of Tokyo City and hierarchical dynamical structure. Chaos, Solitons & Fractals16 (1): 151-65.
124.
Matteo, Matteucci, and Lorenzo Mussone .2006. Ant colony optimization technique for equilibrium assignment in congested transportation networks. GECCO2006:87-88.
125.
Matthews, B., Nigel G. Gilbert, Alan Roach, J. Gary Polhill, and Nick M. Gotts .2007. Agent-based land-use models: A review of applications. Landscape Ecology22:1447-59.
126.
Matthews, K.2001. Applying genetic algorithms to multi-objective land-use planning. PhD diss., Macaulay Land Use Research Institute , Aberdeen, UK.
127.
Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Telle, and E. Teller.1953. Equations of state calculations by fast computing machines. Journal of Chemical Physics21:1087-92.
128.
Michael, B. Lowry.2004. Coordinated regional and city planning using a genetic algorithm. Master’s thesis, Brigham Young University , Provo, UT. http://patriot.lib.byu.edu/ETD/image/etd444.pdf
129.
Michaela, Buergle, Michael Loechl, Urs Waldner, and Kay W. Axhausen .2005. Land use and transport simulation: Applying urbanism in the greater Zurich area. http://www.ivt.ethz.ch/vpl/publications/reports/ab292.pdf
130.
Michel, Hagoort, Stan Geertman, and Henk Ottens.2007. Spatial externalities, neighbourhood rules and CA land-use modelling. Annals of Regional Science42:39-56.
131.
Michel, Jules Dreyfus-Leon1999. Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning. Ecological Modelling120:2-3.
132.
Millan, W., A. Clark, and E. Dawson.1997. Smart hill climbing finds better Boolean functions. Workshop on Selected Areas in Cryptology 1997, Workshop Record, 50-63.
133.
Miyagi, T.2004. A modelling of route choice behaviour in transportation networks: An approach from reinforcement leaning. Urban Transport10:235-44.
134.
Milner-Gulland, E.J., C. Kerven, R. Behnke, I.A. Wright, and A. Smailov.2006. A multi-agent system model of astoralist behaviour in Kazakhstan. Ecological Complexity3 (1): 23-36.
135.
Mohamad, E.A., M.M. Mansour, S. EI-Debeiky, K.G. Mohamad, N.D. Rao, and G. Ramakrishna.1996. Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface . Electric Power System Research39 (3): 171-77.
136.
Moller-Jensen, L., 1990. Knowledge-based classification of an urban area using texture and context information in Landsat-TM imagery. Photogrammetric Engineering and Remote Sensing56 (6), pp. 899-904.
137.
Moller-Jensen, L.1997. Classification of urban land cover based on expert systems, object models and texture. Computers, Environment and Urban Systems21:291-302.
138.
Mota, G.L.A., Feitosa, R.Q., Coutinho, H.L.C., Liedtke, C.-E., Muller, S., Pakzad, K., Meirelles, M.S.P.2007. Multitemporal fuzzy classification model based on class transition possibilities. ISPRS Journal of Photogrammetry and Remote Sensing, 62 (3), pp. 186-200.
139.
Nara, Atsushi, and Paul M. Torrens .2005. Simulating inner-city gentrification using hybrid models of cellular automata and multi-Agent Systems. Presented at 8th International Conference on GeoComputation, University of Michigan, Ann Arbor, July 31.
140.
Niandry, Moreno, Raquel Quintero, Magdiel Ablan, Rodrigo Barros, Jacinto Dávila, Hirma Ramírez, Giorgio Tonella, and Miguel F. Acevedo .2007. Biocomplexity of deforestation in the Caparo Tropical Forest Reserve in Venezuela: An integrated multi-agent and cellular automata model. Environmental Modelling and Software22 (5): 664-73.
141.
Nicholas, Gazulis, and Keith C. Clarke .2006. Exploring the DNA of our regions: Classification of outputs from the SLEUTH model. ACRI2006:462-71.
142.
Norte Pinto, Nuno, and António Pais Antunes.2007. Cellular automata and urban studies: A literature survey. ACE: Architecture, City and Environment1 (3): 368-99.
143.
Oh, K., and Y. Jeong.2002. The usefulness of the GIS-fuzzy set approach in evaluating the urban residential environment. Environment and Planning B: Planning and Design29 (4): 589-606.
144.
Openshaw, S., and C. Openshaw.1997. Artificial intelligence in geography . New York: John Wiley.
Owen, S.M., A.R. MacKenzie, R.G.H. Bunce, et al. 2006. Urban land classification and its uncertainties using principal component and cluster analyses: A case study for the UK West Midlands. Landscape and Urban Planning78 (4): 311-21.
147.
Pallav, Negi.2006. Artificial immune system based urban traffic control. PhD thesis, Texas A&M University, Corpus Christi.
148.
Papini, L., G.A. Rabino, A. Colonna, V. Di Stefano, and S. Lombardo.1998. Learning cellular automata in a real world: The case study of the Rome metropolitan area. ACRI’96: Proceedings of the Third Conference on Cellular Automata for Research And Industry, 165-83. London: Springer-Verlag.
149.
Parker, D.C., S.M. Manson, M.A. Janssen, M.J. Hoffmann, and P. Deadman.2003. Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers93:314-37.
150.
Passone, S., P.W.H. Chung, and V. Nassehi.2002. Case-based reasoning for estuarine model design. Lecture Notes Artificial Intelligence24 (16): 590-603.
151.
Pijanowski, B.C., Daniel G. Brown, Bradley A. Shellito, and Gaurav A. Manik .2002. Using neural networks and GIS to forecast land use changes: A land transformation mode. Computers, Environment and Urban Systems26 (5): 553-75.
152.
Pijanowski, B.C., S. Pithadia, B.A. Shellito, and K. Alexandridis .2005. Calibrating a neural network based urban change model for two metropolitan areas of the upper Midwest of the United States. International Journal of Geographical Information Science19 (2): 197-215.
153.
Pinto, N.2006. Microsimulation approach for modelling the growth of small urban areas. Master’s thesis, Civil Engineering Department, School of Engineering of the University of Porto, Portugal.
154.
Piyathamrongchai, K., and M. Batty.2007. Integrating cellular automata and regional dynamics using GIS. Modelling Land-Use Change90:259-77.
155.
Plant, R.E., and M.P. Vayssieres .2000. Combining expert system and GIS technology to implement a state-transition model of oak woodlands. Computers and Electronics in Agriculture27:71-94.
156.
Qin, F., H.Y. Wang, and Z.H. Wang.2007. Research on the methods of automatic-generation of 3D virtual city. Geoinformatics 2007: Geospatial Information Science6753:Z7531.
157.
Rajkishore, Prasad, and A.K. Sinha.2002. IRS-1B based FCC image interpretation for land use and land cover mapping: An expert system approach. Map India, New Delhi.
158.
Rocha, P., J.C. Ferreira, J. Simoes, and J.A. Tenedorio.2007. Modelling coastal and land use evolution patterns through neural network and cellular automata integration. Proceedings of the 9th International Coastal Symposium, 827-31.
159.
Roda, I.R., M. Sanchez-Marre, J. Comas, U. Cortes, and M. Poch.2001. Development of a case-based system for the supervision of an activated sludge process. Environmental Technology22:477-86.
160.
Rogers, James L., Collin M. McCulley, and Christina L. Bloebaum .1996. Integrating a genetic algorithm into a knowledge-based system for ordering complex design processes. Technical Report: NASA-96-icai.jlr. Langley, VA: NASA Langley Technical Report Server.
161.
Rossiter, R.G.1990. ALES: A framework for land evaluation using a microcomputer . Soil Use and Management5:7-20.
Semboloni, F.2000. The dynamic of an urban cellular automata model in a 3-D spatial pattern. Presented at XXI National Conference , Aisre: Regional and Urban Growth in a Global Market, Palermo, Italy.
164.
Shi, X., A.X. Zhu, J. Burt, et al. 2004. A case-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal68 (3): 885-94.
165.
Shie-Yui, Liong, and M. Atiquzzaman.2004. Optimal design of water distribution network using shuffled complex evolution. Journal of the Institution of Engineers44 (1): 93 -107.
166.
Sietchiping, Remy.2004. A geographic information systems and cellular automata-based model of informal settlement growth. PhD thesis, SAGES-School of Anthropology, Geography and Environmental Studies, University of Melbourne, Australia.
167.
Silva, E.A.2001. The DNA of our regions. Artificial intelligence in regional planning. Presented at World Planning Schools Congress (Paper 5019), Shanghai, China.
168.
Silva, Elisabete A.2004. The DNA of our regions: Artificial intelligence in regional planning. Futures36 (10): 1077-94.
169.
Silva, Elisabete A.2006. Waves of Complexity: bifurcations or phase transitions , paper presented in IIIRD Meeting of AESOP Thematic Group on Complexity, Cardiff, UK, May 4-5.
170.
Silva, Elisabete A.2008a. Complexity and CA, and application to metropolitan areas . In A planners meeting with complexity, ed. G. D. Roo and Elisabete A. Silva.Aldershot, UK: Ashgate .
171.
Silva, Elisabete A.2008b. Waves of complexity. Theory, models, and practice. In A planners meeting with complexity, ed. G. D. Roo and Elisabete A. Silva.Aldershot, UK: Ashgate.
172.
Silva, E.A., and K.C. Clarke.2002. Calibration of the SLEUTH urban growth model for Lisbon and Porto. Computer, Environment and Urban Systems26:525-52.
173.
Silva, Elisabete A., and Keith Clarke.2005. Complexity, emergence and cellular urban models: Lessons learned from appling SLEUTH to two Portuguese cities. European Planning Studies13 (1): 93-115.
174.
Silva, Elisabete A., J. Wileden, and J. Ahern.2008. Strategies for landscape ecology in metropolitan planning: Applications using cellular automata models. Progress in Planning70 (4): 133-77.
175.
Silva, E.A., Wileden, J. and Ahern, J.2008b. Strategies for Landscape Ecology in Metropolitan Planning: Applications Using Cellular Automata Models. Progress in Planning70(4):133-177.
176.
Srinivasan, D., Ruey Long Cheu, and Young Peng Poh .2001. Hybrid fuzzy logic-genetic algorithm technique for automated detection of traffic incidents on freeways . Proceedings of 2001 IEEE, Intelligent Transportation Systems, 352-57.
177.
Stefanov, W.L., M.S. Ramsey, and P.R. Christensen .2001. Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens. Environ77:173-85.
178.
Steven, J. D’Amico, Shoou-Jiun Wang, Rajan Batta, and Christopher M. Rump.2002. A simulated annealing approach to police district design. Computers & Operations Research29 (6): 667-84.
179.
Stevens, D., S. Dragicevic, and K. Rothley.2007. A GIS-CA modelling tool for urban planning and decision making. Environmental Modelling & Software22 (6): 761-73.
180.
Stuckens, J., P.R. Coppin, and M.E. Bauer.2000. Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sensing of Environment71:282-96.
181.
Sudhira, H.S.2004. Integration of agent-based and cellular automata models for simulating urban sprawl. http://www.itc.nl/library/Academic_output/2004/MSc_theses_2004.asp
182.
Sudhira, H.S., T.V. Ramachandra, Andreas Wytzisk, and C. Jeganathan.2005. Framework for integration of agent-based and cellular automata models for dynamic geospatial simulations. Technical Report 100, Centre for Ecological Sciences, Indian Institute of Science, Bangalore.
183.
Spall, J.C.2003. Introduction to stochastic search and optimization: Estimation, simulation, and control. John Wiley & Sons, Inc., New York, 595 pp.
184.
Tang, J., L. Wang, and S.W. Myint.2007. Improving urban classification through fuzzy supervised classification and spectral mixture analysis. International Journal of Remote Sensing28:4047-63.
185.
Teodorovic, D., and M. Dell’Orco .2005. Bee colony optimization-A cooperative learning approach to complex transportation problems. Advanced OR and AI Methods in Transportation. Proceedings of the 10th Meeting of the EURO Working Group on Transportation, September 2005, Poznan, Poland, 51-60.
186.
Teodorovic, D., and M. Dell’Orco .2008. Mitigating traffic congestion: Solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology31:135-52.
187.
Teodorovic, D., and P. Lucic.2005. Schedule synchronization in public transit by fuzzy ant system. Transportation Planning and Technology28:47-77.
188.
Theodor, J. Stewart, Ron Janssen, and Marjan van Herwijnen .2004. A genetic algorithm approach to multiobjective land use planning. Computers and Operations Research Archive31 (14): 2293-2313.
189.
Tobler, W.R.1979. Cellular geography. In Philosophy in geography, 379-86. Dordrecht, the Netherlands: Reidel.
190.
Torrens, P., and D. O’Sullivan .2000. Cities, cells, and complexity: Developing a research agenda for urban geocomputation. Presented at 5th International Conference on Geocomputation, University of Greenwich, London.
191.
Torrens, P., and D. O’Sullivan .2001. Editorial: Cellular automata and urban simulation: Where do we go from here? Environment and Planning B: Planning and Design28:163-68.
192.
Tseng, M.-H., et al. 2007. A genetic algorithm rule-based approach for land-cover classification. International Journal of Photogrammetry and Remote Sensing63 (2): 202-12.
193.
Tzai-Zang, Lee, Chien-Hsing Wu, and Hsien-Hui Wei .2008. KBSLUA: A knowledge-based system applied in river land use assessment. Expert Systems with Applications34 (2): 889-99.
194.
Venkataraman, P.2001. Applied optimization with Matlab programming. New York: John Wiley.
195.
Verburg, P. Schot, M. Dijst, and A. Veldkamp.2004. Land use change modelling: Current practice and research priorities. Geojournal61:309-24.
196.
Voorhees, A.M.1959. Land use and traffic models. Journal of the American Institute of Planners25:55-57.
197.
Wagner, P., and M. Wegener.2007. Urban land use, transport and environment models: Experiences with an integrated mircroscopic approach. Dis P170 (3/2000): 45-56.
198.
Wahle, J., and M. Schreckenberg .2001. A multi-agent system for on-line simulations based on real-world traffic data. Presented at Hawaii International Conference on System Science, IEEE Computer Society.
199.
Wang, Changyao, Luo Chengfeng, and Liu Zhengjun .2005. Integrating genetic algorithm method with neural network for land use classification using SZ-3 CMODIS data . Progress in Natural Science15(10): 937-942.
200.
Wang, W., X.J. Feng, and Y. Wang.2007. The optimization study on passenger organization at large-scale passenger station during rush hours based on multi-agent and cellular automata. 2007 IEEE International conference on automation and logistics11 (6): 121-25.
201.
Webster, C.1996. Urban morphological fingerprints. Environment and Planning B: Planning and Design23:279-97.
202.
Williamson, P., M. Birkin, and P. Rees.1998. The estimation of population microdata using data from small area statistics and samples of anonymised records. Environment and Planning A30 (6): 785-816.
203.
Wilson, Alan.2008. Urban and regional dynamics-3: ‘‘DNA’’ and ‘‘genes’’ as a basis for constructing a typology of areas. CASA Working Paper 130. Centre for Advanced Spatial Analysis (UCL), London, UK.
204.
Witlox, F.2003. MATISSE: A relational expert system for industrial site selection. Expert Systems with Applications24 (1): 133-44.
205.
Witlox, F.2005. Expert systems in land-use planning: An overview. Expert Systems with Applications29 (2): 437-45.
206.
Wong, S.C., C.K. Wong, and C.O. Tong.2001. A parallelized genetic algorithm for the calibration of Lowry model. Parallel Computing27 (12): 1523-36.
207.
Wu, F.1998. Simulating urban encroachment on rural land with fuzzy-logic-controlled cellular automata in a geographical information system. Journal of Environmental Management53 (4): 293-308.
208.
Wu, F.L., and D. Martin.2002. Urban expansion simulation of southeast England using population surface modelling and cellular automata. Environment and Planning A34:1855-76.
209.
Wu, Ning, and Elisabete A. Silva .2009. Artificial intelligence and ‘‘waves of complexity’’ for urban dynamics. WSEAS. Recent Advances in Artificial Intelligence, Knowledge Engineering and Databases, 459-69.
210.
Wu, Ning, and Elisabete A. Silva . n.d.-a. Artificial intelligence and ‘‘waves of complexity’’ for urban dynamics.
211.
Wu, N., and E.A. Silva. n.d.-b. Dynamic models in urban land studies- Exploring sophisticated modeling for urban land dynamics.
212.
Li X., Liu X P.2008. Embedding sustainable development strategies in agent-based models for use as a planning tool. International Journal of Geographical Information Science, 22(1), 21-45.
213.
Xu, Xibao, Jianming Zhang, and Xianjian Zhou.2006. Integrating GIS, cellular automata and genetic algorithm in urban spatial optimization: A case study of Lanzhou. Geoinformatics 2006: Geospatial Information Science6420:1-10.
214.
Yasser, Hammam, Antoni Moorea, and Peter Whighama.2007. The dynamic geometry of geographical vector agents. Computers, Environment and Urban Systems31 (5): 502-19.
215.
Yeh, A.G., and X. Li.2003. Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogrammetric Engineering and Remote Sensing69:1043-52.
216.
Yizengaw, T., and W. Verheye.1995. Application of computer captured knowledge in land evaluation, using ALES in central Ethiopia. Geoderma66 (3-4): 297-311.
217.
Zadeh, L.A.1965. Fuzzy sets. Information and Control8 (3): 338-53.
218.
Zecchin, A.C., A.R. Simpson, H.R. Maier, M. Leonard, A.J. Roberts, and M.J. Berrisford .2006. Application of two ant colony optimisation algorithms to water distribution system optimization. Mathematical and Computer Modelling44:451-68.
219.
Zhang, Liquan, and Zhen Yu.2006. An artificial neural network model of the landscape pattern in Shanghai metropolitan region. Frontiers of Biology in China1:463-69.
220.
Zhang, X., and M.P. Armstrong.2008. Genetic algorithms and the corridor location problem: Multiple objectives and alternative solutions. Environment and Planning B: Planning and Design35:148-68.
221.
Zhou, J., and D. Civco.1996. Using genetic learning neural networks for spatial decision making in GIS. Photogrammetric Engineering & Remote Sensing62:1287-95.
222.
Zhu, X., R.J. Aspinall, and R.G. Healey.1996. ILUDSS: A knowledge based spatial decision support system for strategic land-use planning. Computers and Electronics in Agriculture15:279-301.
