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Article: Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning

TitleSimulation of development alternatives using neural networks, cellular automata, and GIS for urban planning
Authors
Issue Date2003
PublisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/pers
Citation
Photogrammetric Engineering And Remote Sensing, 2003, v. 69 n. 9, p. 1043-1052 How to Cite?
AbstractThis study integrates neural networks and cellular automata (CA) to simulate development alternatives for planning purposes. Most of the existing CA just focus on simulating realistic urban dynamics. This paper demonstrates that development alternatives can be simulated by incorporating planning objectives in CA. It is important to define appropriate parameter values for simulating development alternatives according to the planning objectives of planners and decision makers. Training neural networks can automatically yield the parameter values for urban simulation. GIS and remote sensing provide the training data for calibrating the model. However, the simulation can inherit past land-use problems if the original training data are used to calibrate the model. The original data should be assessed and modified so that the model can remember the past "failure" in land development. Planning objectives can thus be embedded in the model by properly modifying the training data sets. The training is robust because it is based on the well-defined back-propagation algorithm. Experiments were carried out by using the city of Dongguan, China as an example to test the model.
Persistent Identifierhttp://hdl.handle.net/10722/89852
ISSN
2015 Impact Factor: 1.288
2015 SCImago Journal Rankings: 0.830
References

 

DC FieldValueLanguage
dc.contributor.authorYeh, AGOen_HK
dc.contributor.authorLi, Xen_HK
dc.date.accessioned2010-09-06T10:02:36Z-
dc.date.available2010-09-06T10:02:36Z-
dc.date.issued2003en_HK
dc.identifier.citationPhotogrammetric Engineering And Remote Sensing, 2003, v. 69 n. 9, p. 1043-1052en_HK
dc.identifier.issn0099-1112en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89852-
dc.description.abstractThis study integrates neural networks and cellular automata (CA) to simulate development alternatives for planning purposes. Most of the existing CA just focus on simulating realistic urban dynamics. This paper demonstrates that development alternatives can be simulated by incorporating planning objectives in CA. It is important to define appropriate parameter values for simulating development alternatives according to the planning objectives of planners and decision makers. Training neural networks can automatically yield the parameter values for urban simulation. GIS and remote sensing provide the training data for calibrating the model. However, the simulation can inherit past land-use problems if the original training data are used to calibrate the model. The original data should be assessed and modified so that the model can remember the past "failure" in land development. Planning objectives can thus be embedded in the model by properly modifying the training data sets. The training is robust because it is based on the well-defined back-propagation algorithm. Experiments were carried out by using the city of Dongguan, China as an example to test the model.en_HK
dc.languageengen_HK
dc.publisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/persen_HK
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensingen_HK
dc.titleSimulation of development alternatives using neural networks, cellular automata, and GIS for urban planningen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0099-1112&volume=69 &issue=9&spage=1043&epage=1052&date=2003&atitle=Simulation+of+Development+Alternatives+Using+Neural+Networks,+Cellular+Automata,+and+GIS+for+Urban+Planningen_HK
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.scopuseid_2-s2.0-0042421778en_HK
dc.identifier.hkuros92828en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0042421778&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume69en_HK
dc.identifier.issue9en_HK
dc.identifier.spage1043en_HK
dc.identifier.epage1052en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK
dc.identifier.scopusauthoridLi, X=34872691500en_HK

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