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Article: Calibration of cellular automata by using neural networks for the simulation of complex urban systems

TitleCalibration of cellular automata by using neural networks for the simulation of complex urban systems
Authors
Issue Date2001
PublisherPion Ltd. The Journal's web site is located at http://www.envplan.com
Citation
Environment And Planning A, 2001, v. 33 n. 8, p. 1445-1462 How to Cite?
AbstractThis paper presents a new cellular automata (CA) model which uses artificial neural networks for both calibration and simulation. A critical issue for urban CA simulation is how to determine parameter values and define model structures. The simulation of real cities involves the use of many variables and parameters. The calibration of CA models is very difficult when there is a large set of parameters. In the proposed model, most of the parameter values for CA simulation are automatically determined by the training of artificial neural networks. The parameter values from the training are then imported into the CA model which is also based on the algorithm of neural networks. With the use of neural networks, users do not need to provide detailed transition rules which are difficult to define. The study shows that the model has better accuracy than traditional CA models in the simulation of nonlinear complex urban systems.
Persistent Identifierhttp://hdl.handle.net/10722/89877
ISSN
2015 Impact Factor: 1.46
2015 SCImago Journal Rankings: 1.460
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_HK
dc.contributor.authorYeh, AGen_HK
dc.date.accessioned2010-09-06T10:02:54Z-
dc.date.available2010-09-06T10:02:54Z-
dc.date.issued2001en_HK
dc.identifier.citationEnvironment And Planning A, 2001, v. 33 n. 8, p. 1445-1462en_HK
dc.identifier.issn0308-518Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/89877-
dc.description.abstractThis paper presents a new cellular automata (CA) model which uses artificial neural networks for both calibration and simulation. A critical issue for urban CA simulation is how to determine parameter values and define model structures. The simulation of real cities involves the use of many variables and parameters. The calibration of CA models is very difficult when there is a large set of parameters. In the proposed model, most of the parameter values for CA simulation are automatically determined by the training of artificial neural networks. The parameter values from the training are then imported into the CA model which is also based on the algorithm of neural networks. With the use of neural networks, users do not need to provide detailed transition rules which are difficult to define. The study shows that the model has better accuracy than traditional CA models in the simulation of nonlinear complex urban systems.en_HK
dc.languageengen_HK
dc.publisherPion Ltd. The Journal's web site is located at http://www.envplan.comen_HK
dc.relation.ispartofEnvironment and Planning Aen_HK
dc.titleCalibration of cellular automata by using neural networks for the simulation of complex urban systemsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0308-518X&volume=33&spage=1445&epage=1462&date=2001&atitle=Calibration+of+Cellular+Automata+By+Using+Neural+Networks+for+the+Simulation+of+Complex+Urban+Systemsen_HK
dc.identifier.emailYeh, AG: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AG=rp01033en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1068/a33210en_HK
dc.identifier.scopuseid_2-s2.0-0034837081en_HK
dc.identifier.hkuros67209en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034837081&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume33en_HK
dc.identifier.issue8en_HK
dc.identifier.spage1445en_HK
dc.identifier.epage1462en_HK
dc.identifier.isiWOS:000170992200015-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, X=34872691500en_HK
dc.identifier.scopusauthoridYeh, AG=7103069369en_HK

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