A fuzzy cellular automata modeling approach - Accessing urban growth dynamics in linguistic terms

Mantelas, LA; Hatzichristos, T; Prastacos, P

dc.contributor.author	Mantelas, LA	en
dc.contributor.author	Hatzichristos, T	en
dc.contributor.author	Prastacos, P	en
dc.date.accessioned	2014-03-01T02:46:36Z
dc.date.available	2014-03-01T02:46:36Z
dc.date.issued	2010	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32744
dc.subject	Cellular Automata	en
dc.subject	Fuzzy Logic	en
dc.subject	Mesogia Athens	en
dc.subject	Rule-based Modeling	en
dc.subject	Urban Growth	en
dc.subject.other	Data requirements	en
dc.subject.other	Fuzzy cellular automata	en
dc.subject.other	Information flows	en
dc.subject.other	Input variables	en
dc.subject.other	Knowledge base	en
dc.subject.other	Linguistic terms	en
dc.subject.other	Methodological frameworks	en
dc.subject.other	Multi-level	en
dc.subject.other	Parallel connections	en
dc.subject.other	Rule based	en
dc.subject.other	Rule-based Modeling	en
dc.subject.other	Single variable	en
dc.subject.other	Urban growth	en
dc.subject.other	Urban modeling	en
dc.subject.other	Automata theory	en
dc.subject.other	Cellular automata	en
dc.subject.other	Electric connectors	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Information management	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Linguistics	en
dc.subject.other	Pattern recognition systems	en
dc.subject.other	Robots	en
dc.subject.other	Translation (languages)	en
dc.subject.other	Fuzzy logic	en
dc.title	A fuzzy cellular automata modeling approach - Accessing urban growth dynamics in linguistic terms	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-12156-2-11	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-12156-2-11	en
heal.publicationDate	2010	en
heal.abstract	This paper presents a methodological framework for urban modeling which accesses the multi-level urban growth dynamics and expresses them in linguistic terms. In this approach a set of parallel fuzzy systems is used, each one of which focuses on different aspects of the urban growth dynamics, different drivers or restriction of development and concludes over the suitability for urbanization for each area. As a result the systems' structure and connection merge the input variables into a single variable providing an information flow familiar to the human conceptualization of the phenomenon. At the same time, the structure does not pose severe data requirements while the utilization of parallel connection between fuzzy systems allows the user to add or remove variables without altering the ways in which other variables affect the knowledge base. Following, a fuzzy system that incorporates cellular automata techniques simulates the horizontal and vertical urban growth. The proposed model is applied and tested in the Mesogeia area in the Attica basin (Athens) and fits reality in average by 76% (LeeShalle index) while the average cell error is 19%. Nevertheless, the benefits obtained in the herein presented approach lie in the information management and representation. © 2010 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-12156-2-11	en
dc.identifier.volume	6016 LNCS	en
dc.identifier.issue	PART 1	en
dc.identifier.spage	140	en
dc.identifier.epage	151	en