FUNDAMENTOS TEÓRICOS DE MODELAGEM EM SISTEMAS COMPLEXOS
DOI :
https://doi.org/10.28998/contegeo.4i7.8363Mots-clés :
Sistemas Complexos, Modelagem, Autômatos Celulares, Modelos Baseados no Agente, Modelos HíbridosRésumé
A geografia tem por definição o estudo do conjunto da Terra, que é por sua natureza extremamente complexo e depende de um número inesgótavel de elementos e relações entre os sistemas que o compõem. A complexidade do mundo atrai cada vez mais a comunidade científica com o inuito de melhor entender e representar as inúmeras interações que ocorrem na superfície, sobretudo no campo da geografia. A partir da teoria da complexidade várias foram as técnicas de modelagem computacional desenvolvidas a fim de simular o mundo real e antecipar possíveis eventos, como por exemplo a expansão urbana de uma determinada cidade, o desmatamento de uma área devido ao avanço da agricultura, e até mesmo padrões de imigração de uma população. É consenso que a modelagem será cada vez mais utilizada no planejamento territorial e ambiental, e este artigo traz uma breve explanação de alguns dos métodos mais comuns utlizados para estes fins.
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Références
ABDOU, M.; HAMILL, L.; GILBERT, N. Designing and Building an Agent-Based Model. In: HEPPENSTALL, A. J. et al. (Eds.). . Agent-Based Models of Geographical Systems. 1. ed. [s.l.] Springer Netherlands, 2012. p. 760.
AHMED, B.; AHMED, R.; ZHU, X. Evaluation of Model Validation Techniques in Land Cover Dynamics. ISPRS International Journal of Geo-Information, v. 2, n. 3, p. 577–597, 2013.
ALMEIDA, C. M. DE; BATTY, M. Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Computers, Environment and Urban Systems, v. 27, p. 481–509, 2003.
ARSANJANI, J. J. et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, v. 21, n. 1, p. 265–275, 2012.
ARSANJANI, T. J. et al. Spatiotemporal monitoring of Bakhtegan Lake’s areal fluctuations and an exploration of its future status by applying a cellular automata model. Computers and Geosciences, v. 78, p. 37–43, 2015.
AUMANN, C. A. A methodology for developing simulation models of complex systems. Ecological Modelling, v. 202, n. 3–4, p. 385–396, abr. 2007.
AZARI, M. et al. Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran. GIScience & Remote Sensing, v. 1603, n. January, p. 1–23, 2016.
BALBI, S. et al. A spatial agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine tourism destination. Environmental Modelling & Software, v. 45, p. 29–51, jul. 2013.
BATTY, M. A Generic Framework for Computational Spatial Modelling. In: HEPPENSTALL, A. . (Ed.). . Agent-Based Models of Geographical Systems. [s.l.] Springer Science, 2012. p. 19–32.
BATTY, M.; TORRENS, P. M. Modelling and prediction in a complex world. Futures, v. 37, n. 7, p. 745–766, set. 2005.
BONE, C.; DRAGICEVIC, S.; ROBERTS, A. A fuzzy-constrained cellular automata model of forest insect infestations. Ecological Modelling, v. 192, n. 1–2, p. 107–125, fev. 2006.
BONNELL, T. R. et al. An agent-based model of red colobus resources and disease dynamics implicates key resource sites as hot spots of disease transmission. Ecological Modelling, v. 221, n. 20, p. 2491–2500, out. 2010.
BROWN, D.; ROBINSON, D. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 2006.
CÉSAR, E. S.; VALENTE, M. S.; DIAS, P. V. Modelação Espacial de Incêndios Florestais: autómatos celulares. p. 1–6, 2012.
CHEN, S. H.; JAKEMAN, A. J.; NORTON, J. P. Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and Computers in Simulation, v. 78, n. 2–3, p. 379–400, jul. 2008.
CROOKS, A. T.; CASTLE, C. J. E. The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation. In: HEPPENSTALL, A. J. (Ed.). . Agent-Based Models of Geographical Systems. [s.l.] Springer Science, 2012. p. 8–10.
DANTAS, A.; MEDEIROS, T. Espaço e Modernidade. In: Introdução à Ciência Geográfica. Natal, Brésil: EDUFRN, 2008. p. 176.
FILATOVA, T. et al. Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environmental Modelling & Software, v. 45, p. 1–7, jul. 2013.
FONTES GONÇALVES, V. M. COMPLEXIDADE : TEORIA E PRÁTICA INTERDISCIPLINAR. Caderno de Filosofia e Psicologia da Educação, p. 49–75, 2005.
GAUDREAU, J.; PEREZ, L.; DRAPEAU, P. BorealFireSim: A GIS-based Cellular Automata Model of Wildfires for the Boreal Forest of Quebec in a Climate Change Paradigm. Ecological Informatics, v. 32, p. 12–27, 2016.
GHOSH, P. et al. Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review. Remote Sensing Applications: Society and Environment, v. 5, n. January, p. 64–77, 2017.
GRINBLAT, Y.; GILICHINSKY, M.; BENENSON, I. Cellular Automata Modeling of Land-Use/Land-Cover Dynamics: Questioning the Reliability of Data Sources and Classification Methods. Annals of the American Association of Geographers, v. 106, n. 6, p. 1299–1320, 2016.
HAMDY, O. et al. Applying a Hybrid Model of Markov Chain and Logistic Regression to Identify Future Urban Sprawl in Abouelreesh, Aswan: A Case Study. Geosciences, v. 6, n. 4, p. 43, 2016.
HATTERMANN, F. F.; KRYSANOVA, V.; HESSE, C. Modelling wetland processes in regional applications. Hydrological Sciences Journal, v. 53, n. 5, p. 1001–1012, 2008.
HOSMER, D. W. et al. A COMPARISON OF GOODNESS-OF-FIT TESTS FOR THE LOGISTIC REGRESSION MODEL. STATISTICS IN MEDICINE Ltd. Stat. Med, v. 16, n. 16, p. 965–980, 1997.
HYANDYE, C.; MARTZ, L. W. A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International Journal of Remote Sensing, v. 38, n. 1, p. 64–81, 2 jan. 2017.
KAMUSOKO, C.; GAMBA, J. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS International Journal of Geo-Information, v. 4, n. 2, p. 447–470, 1 abr. 2015.
KUMAR, K. S.; KUMARI, K. P.; BHASKAR, P. U. Application of Markov Chain & Cellular Automata based model for prediction of Urban transitions. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016, p. 4007–4012, 2016.
LANGLOIS, P. Cellular automata for modeling spatial systems. The Modeling Process in Geography: From Detreminism to Complexity, p. 278–306, 2008.
LANGLOIS, P. Simulation des systèmes complexes en géographie : fondements théoriques et applications. 1. ed. Paris: Lavoisier, 2010.
LIN, Y.-P. et al. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, v. 25, n. 1, p. 65–87, 2011.
LIU, Y.; DAI, L.; XIONG, H. Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models. Journal of Environmental Planning and Management, v. 58, n. 6, p. 1113–1136, 2015.
LUO, G. et al. Dynamics of landscape patterns in an inland river delta of Central Asia based on a cellular automata-Markov model. Reg Environ Change, v. 15, p. 277–289, 2015.
MANSON, S. Simplifying complexity: a review of complexity theory. Geoforum, v. 32, n. 3, p. 405–414, ago. 2001.
MANSON, S.; O’SULLIVAN, D. Complexity theory in the study of space and place. Environment and Planning A, v. 38, n. 4, p. 677–692, 2006.
MAS, J.-F. et al. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, v. 51, p. 94–111, 2014.
MOGHADAM, H. S.; HELBICH, M. Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, v. 40, p. 140–149, 2013.
MOINE, A. Le territoire comme un système complexe. Des outils pour l ’ aménagement et la géographie. Septièmes rencontres de Théo Quant, janvier 2005, p. 11 P, 2005.
MUNSHI, T. et al. Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities, v. 39, p. 68–86, 2014.
O’SULLIVAN, D. Complexity science and human geography. Transactions of the Institute of British Geographers, v. 29, n. 3, p. 282–295, set. 2004.
OZTURK, D. Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Remote Sensing, v. 7, n. 5, p. 5918–5950, 13 maio 2015.
POOYANDEH, M.; MARCEAU, D. J. A spatial web/agent-based model to support stakeholders’ negotiation regarding land development. Journal of environmental management, v. 129C, p. 309–323, 22 ago. 2013.
RYKIEL, E. J. Testing ecological models: The meaning of validation. Ecological Modelling, v. 90, n. 3, p. 229–244, 1996.
SMAJGL, A.; BOHENSKY, E. Behaviour and space in agent-based modelling: Poverty patterns in East Kalimantan, Indonesia. Environmental Modelling & Software, v. 45, p. 8–14, 2013.
SOHL, T. et al. Modeled historical land use and land cover for the conterminous United States. Journal of Land Use Science, v. 11, n. 4, p. 476–499, 2016.
STRAATMAN, B.; WHITE, R.; ENGELEN, G. Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, v. 28, n. 1–2, p. 149–170, 2004.
TAYYEBI, A.; PERRY, P. C.; TAYYEBI, A. H. Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster-vector routines with remote sensing and GIS. International Journal of Geographical Information Science, v. 28, n. 4, p. 639–659, 2014.
TINÉ, M.; PEREZ, L.; MOLOWNY-HORAS, R. Hybrid spatiotemporal simulation of future changes in open wetlands : A study of the Abitibi-Témiscamingue region , Québec , Canada. International Journal of Appl Earth Observation and Geoinformation, v. 74, n. July 2018, p. 302–313, 2019.
TORRENS, P. M.; BENENSON, I. Geographic Automata Systems. International Journal of Geographical Information Science, v. 19, n. 4, p. 385–412, abr. 2005.
VIDAL DE LA BLACHE, P. Des caractères distinctifs de la géographie. Annales de Géographie, v. 22, n. 124, p. 289–299, 1913.
WU, F. Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, v. 16, n. 8, p. 795–818, 2002.
YU, H.; HE, Z.; PAN, X. Wetlands shrink simulation using Cellular Automata: A case study in Sanjiang Plain, China. Procedia Environmental Sciences, v. 2, n. 5, p. 225–233, 2010.
YU, J. et al. Cellular automata-based spatial multi-criteria land suitability simulation for irrigated agriculture. International Journal of Geographical Information Science, v. 25, n. 1, p. 131–148, 2011.
