Centre de documentation
scientifique

Data mining of cellular automata's transition rules / X. Li in International journal of geographical information science IJGIS, vol 18 n° 8 (december 2004)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 18 n° 8 (december 2004) . - pp 723 - 744 Titre : Data mining of cellular automata's transition rules Type de document : Article/Communication Auteurs : X. Li, Auteur ; A. Gar-On Yeh, Auteur Année de publication : 2004 Article en page(s) : pp 723 - 744 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] échantillonnage [Termes IGN] exploration de données géographiques [Termes IGN] Hong-Kong [Termes IGN] incertitude des données [Termes IGN] simulation Résumé : (Auteur) This paper presents a new method to discover knowledge for geographical cellular automata (CA) by using a data-mining technique. CA have the ability to simulate complex geographical phenomena. Very few studies have been carried out on how to determine and validate the transition rules of CA from observed data. The transition rules of traditional CA are usually expressed by mathematical equations. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The explicit transition rules are more intuitive to decision-makers. The transition rules are obtained by applying data-mining techniques to spatial data. The proposed method can reduce the uncertainties in defining transition rules and help to generate more reliable simulation results. Numéro de notice : A2004-481 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810410001705325 En ligne : https://doi.org/10.1080/13658810410001705325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26999 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-04081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-04082	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Neural-network-based cellular automata for simulating multiple land use changes using GIS / X. Li in International journal of geographical information science IJGIS, vol 16 n° 4 (june 2002)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 16 n° 4 (june 2002) . - pp 323 - 343 Titre : Neural-network-based cellular automata for simulating multiple land use changes using GIS Type de document : Article/Communication Auteurs : X. Li, Auteur ; A. Gar-On Yeh, Auteur Année de publication : 2002 Article en page(s) : pp 323 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] automate cellulaire [Termes IGN] Chine [Termes IGN] croissance urbaine [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol Résumé : (Auteur) This paper presents a new method to simulate the evolution of multiple land uses based on the integration of neural networks and cellular automata using GIS. Simulation of multiple land use changes using cellular automata (CA) is difficult because numerous spatial variables and parameters have to be utilized. Conventional CA models have problems in defining simulation parameter values, transition rules and model structures. In this paper, a three-layer neural network with multiple output neurons is designed to calculate conversion probabilities for competing multiple land uses. The model involves iterative looping of the neural network to simulate gradual land use conversion processes. Spatial variables are not deterministic because they are dynamically updated at the end of each loop. A GIS is used to obtain site attributes and training data, and to provide spatial functions for constructing the neural network. The parameter values for modelling are automatically generated by the training procedure of neural networks. The model has been successfully applied to the simulation of multiple land use changes in a fast growing area in southern China. Numéro de notice : A2002-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810210137004 En ligne : https://doi.org/10.1080/13658810210137004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22075 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-02041	RAB	Revue	Centre de documentation	En réserve L003	Disponible