File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Neural-network-based cellular automata for realistic and idealized urban simulation

TitleNeural-network-based cellular automata for realistic and idealized urban simulation
基於神經網絡的單元自動機CA及真實和優化的城市模擬
Authors
KeywordsCellular Automata (單元自動機)
Dongguan (東莞)
GIS (地理信息系統)
Neural Networks (神經網絡)
Urban Simulations (城市模擬)
Issue Date2002
PublisherScience Press (科學出版社). The Journal's web site is located at http://www.geog.com.cn/
Citation
Acta Geographica Sinica, 2002, v. 57 n. 2, p. 159-166 How to Cite?
地理學報, 2002, v. 57 n. 2, p. 159-166 How to Cite?
AbstractThere is rapid development of CA models for simulation of land use patterns and urban systems recently. Traditional methods using multicriteria evaluation (MCE) have limitations because they only use a linear weighted combination of multiple factors for predictions. It cannot explain much of the non-linear variations presented in complex urban systems. It is most attractive that neural networks have the capabilities of nonlinear mapping which is critical for actual urban systems. The study indicates that improvement has been made by using the proposed model to simulate non-linear urban systems. The advantages of using neural networks are apparent. The method can significantly reduce much of the tedious work, such as the requirements for explicit knowledge of identify relevant criteria, assign scores, and determine criteria preference. Furthermore, variables used in spatial decision are always dependent on each other. General MCE methods are not suitable to handle relevant variables. Neural networks can learn and generalize correctly and handle redundant, inaccurate or noise data which are frequently found in land use information. Users don't need to worry about which variable should be selected or not. Knowledge and experiences can be easily learnt and stored for further simulation. General CA models also have problems in obtaining consistent parameters when there are many variables in the prediction. It is very time consuming in finding the proper values of parameters for CA models through general calibration procedures. This paper has demonstrated that neural network can be integrated in CA simulation for solving the problems in finding the values of parameters. Users don't need to pay great efforts in seeking suitable parameters or weights which are difficult to be determined by general CA methods. In the proposed method, the parameters or weights required for CA simulation are automatically determined by the training procedures instead of by users. It is convenient to embed the neural network in the CA simulation model based on the platform of GIS. The model is plausible in forecasting urban growth and formulating idealized development patterns. Different scenarios of development patterns can be easily simulated based on proper training using neural networks. Remote sensing data can be used to prepare training data sets for more realistic simulation. Based on planning objectives and development evaluation, original training data sets can be rationally modified to obtain different sets of adjusted weights through the training procedure of neural networks. These adjusted weights can be applied to the CA model in generating idealized patterns.
提出了一種基于神經網絡的單元自動機(CA)。CA已被越來越多地應用在城市及其它地理現象的模擬中。CA模擬所碰到的最大問題是如何確定模型的結構和參數。模擬真實的城市涉及到使用許多空間變量和參數。當模型較復雜時,很難確定模型的參數值。本模型的結構較簡單,模型的參數能通過對神經網絡的訓練來自動獲取。分析表明,所提出的方法能獲得更高的模擬精度,并能大大縮短尋找參數所需要的時間。通過篩選訓練數據,本模型還可以進行優化的城市模擬,為城市規劃提供參考依據。
Persistent Identifierhttp://hdl.handle.net/10722/176281
ISSN
2015 SCImago Journal Rankings: 0.447
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2012-11-26T09:08:13Z-
dc.date.available2012-11-26T09:08:13Z-
dc.date.issued2002en_US
dc.identifier.citationActa Geographica Sinica, 2002, v. 57 n. 2, p. 159-166en_US
dc.identifier.citation地理學報, 2002, v. 57 n. 2, p. 159-166-
dc.identifier.issn0375-5444en_US
dc.identifier.urihttp://hdl.handle.net/10722/176281-
dc.description.abstractThere is rapid development of CA models for simulation of land use patterns and urban systems recently. Traditional methods using multicriteria evaluation (MCE) have limitations because they only use a linear weighted combination of multiple factors for predictions. It cannot explain much of the non-linear variations presented in complex urban systems. It is most attractive that neural networks have the capabilities of nonlinear mapping which is critical for actual urban systems. The study indicates that improvement has been made by using the proposed model to simulate non-linear urban systems. The advantages of using neural networks are apparent. The method can significantly reduce much of the tedious work, such as the requirements for explicit knowledge of identify relevant criteria, assign scores, and determine criteria preference. Furthermore, variables used in spatial decision are always dependent on each other. General MCE methods are not suitable to handle relevant variables. Neural networks can learn and generalize correctly and handle redundant, inaccurate or noise data which are frequently found in land use information. Users don't need to worry about which variable should be selected or not. Knowledge and experiences can be easily learnt and stored for further simulation. General CA models also have problems in obtaining consistent parameters when there are many variables in the prediction. It is very time consuming in finding the proper values of parameters for CA models through general calibration procedures. This paper has demonstrated that neural network can be integrated in CA simulation for solving the problems in finding the values of parameters. Users don't need to pay great efforts in seeking suitable parameters or weights which are difficult to be determined by general CA methods. In the proposed method, the parameters or weights required for CA simulation are automatically determined by the training procedures instead of by users. It is convenient to embed the neural network in the CA simulation model based on the platform of GIS. The model is plausible in forecasting urban growth and formulating idealized development patterns. Different scenarios of development patterns can be easily simulated based on proper training using neural networks. Remote sensing data can be used to prepare training data sets for more realistic simulation. Based on planning objectives and development evaluation, original training data sets can be rationally modified to obtain different sets of adjusted weights through the training procedure of neural networks. These adjusted weights can be applied to the CA model in generating idealized patterns.en_US
dc.description.abstract提出了一種基于神經網絡的單元自動機(CA)。CA已被越來越多地應用在城市及其它地理現象的模擬中。CA模擬所碰到的最大問題是如何確定模型的結構和參數。模擬真實的城市涉及到使用許多空間變量和參數。當模型較復雜時,很難確定模型的參數值。本模型的結構較簡單,模型的參數能通過對神經網絡的訓練來自動獲取。分析表明,所提出的方法能獲得更高的模擬精度,并能大大縮短尋找參數所需要的時間。通過篩選訓練數據,本模型還可以進行優化的城市模擬,為城市規劃提供參考依據。-
dc.languagechien_US
dc.publisherScience Press (科學出版社). The Journal's web site is located at http://www.geog.com.cn/-
dc.relation.ispartofActa Geographica Sinicaen_US
dc.relation.ispartof地理學報-
dc.subjectCellular Automata (單元自動機)en_US
dc.subjectDongguan (東莞)en_US
dc.subjectGIS (地理信息系統)en_US
dc.subjectNeural Networks (神經網絡)en_US
dc.subjectUrban Simulations (城市模擬)en_US
dc.titleNeural-network-based cellular automata for realistic and idealized urban simulationen_US
dc.title基於神經網絡的單元自動機CA及真實和優化的城市模擬-
dc.typeArticleen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0036385397en_US
dc.identifier.hkuros73975-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036385397&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume57en_US
dc.identifier.issue2en_US
dc.identifier.spage159en_US
dc.identifier.epage166en_US
dc.publisher.placeBeijing (北京)en_US
dc.identifier.scopusauthoridLi, X=34872691500en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats