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An artificial-neural-network-based, constrained CA model for simulating urban growth / Q. Guan in Cartography and Geographic Information Science, vol 32 n° 4 (October 2005)  

Public [article]  inCartography and Geographic Information Science > vol 32 n° 4 (October 2005) . - pp 369 - 380 Titre : An artificial-neural-network-based, constrained CA model for simulating urban growth Type de document : Article/Communication Auteurs : Q. Guan, Auteur ; L. Wang, Auteur ; K.C. Clarke, Auteur Année de publication : 2005 Article en page(s) : pp 369 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] données localisées [Termes IGN] modèle mathématique [Termes IGN] occupation du sol [Termes IGN] Pékin (Chine) [Termes IGN] réseau neuronal artificiel [Termes IGN] simulation Résumé : (Auteur) Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and drus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network bas been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand to urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001-2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty. Numéro de notice : A2005-539 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/152304005775194746 En ligne : https://doi.org/10.1559/152304005775194746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27675 [article]

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