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A bottom up approach to the modelling of costal and land use evolution throught GIS

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Resumo:Coastal areas stand in the intersection of human and physical factors, some of them interacting in a non linear fashion and presenting feedbacks, which typically are characteristics of complex systems. This kind of systems are very difficult to model in a top-down approach, due to the high number of variables in question and the nature of relations between them. The spatial dimension itself, also introduces some complexity which is difficult to capture in a deterministic approach. In this study, we propose a bottom-up approach for modelling the coastline evolution, integrating land use cover. The system is based in a probabilistic Cellular Automata (CA) model, which divides the study area in a grid. The hermetic structure of CA is overcomed by using transition rules whose weights have been calibrated by a Artificial Neural Network (ANN). In this way, the knowledge of past events is incorporated into the model and projected into the future, by means of using ”intelligence”; this contrasts with techniques such as linear regression, whose efficacy in the case study was evaluated to be much inferior. Finally, is important to mention that the use of a Geographical information Systems (GIS) environment, enabled to assemble together different types of data and overlap them in an efficient way, which would be very hard or impossible to do otherwise; therefore, we believe that the use of GIS is crucial in spatial based simulations. The case study for this model was an area of the Municipality of Almada (Portugal) that has an extensive coastal line, both Atlantic and estuary. The modelling of spatially dynamic and naturally complex phenomena occurring in these coastal areas, is important for the definition of an innovative strategy for their physical planning and also their environmental management.
Autores principais:Simões, Joana
Outros Autores:Rocha, Jorge; Ferreira, José Carlos; Tenedório, José António; Morgado, Paulo
Assunto:GIS Artificial Neural Networks Planning Cellular Automata Coastal Areas
Ano:2009
País:Portugal
Tipo de documento:capítulo de livro
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Lisboa
Idioma:inglês
Origem:Repositório da Universidade de Lisboa
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author Simões, Joana
author2 Rocha, Jorge
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
author2_role author
author
author
author
author_facet Simões, Joana
Simões, Joana
Rocha, Jorge
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
Rocha, Jorge
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
author_role author
contributor_name_str_mv Repositório Científico de Acesso Aberto da ULisboa
country_str PT
creators_json_str [{\"Person.name\":\"Simões, Joana\"},{\"Person.name\":\"Rocha, Jorge\",\"Person.identifier.orcid\":\"0000-0002-7228-6330\"},{\"Person.name\":\"Ferreira, José Carlos\"},{\"Person.name\":\"Tenedório, José António\"},{\"Person.name\":\"Morgado, Paulo\",\"Person.identifier.orcid\":\"0000-0002-3220-4943\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Repositório Científico de Acesso Aberto da ULisboa
datacite.creators.creator.creatorName.fl_str_mv Simões, Joana
Rocha, Jorge
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
datacite.date.Accepted.fl_str_mv 2009-09-01T00:00:00Z
datacite.date.available.fl_str_mv 2023-06-02T07:42:12Z
datacite.date.embargoed.fl_str_mv 2023-06-02T07:42:12Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
datacite.titles.title.fl_str_mv A bottom up approach to the modelling of costal and land use evolution throught GIS
dc.contributor.none.fl_str_mv Repositório Científico de Acesso Aberto da ULisboa
dc.creator.none.fl_str_mv Simões, Joana
Rocha, Jorge
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
dc.date.Accepted.fl_str_mv 2009-09-01T00:00:00Z
dc.date.available.fl_str_mv 2023-06-02T07:42:12Z
dc.date.embargoed.fl_str_mv 2023-06-02T07:42:12Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10451/57840
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv CoastGIS 2009
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
dc.title.fl_str_mv A bottom up approach to the modelling of costal and land use evolution throught GIS
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_3248
description Coastal areas stand in the intersection of human and physical factors, some of them interacting in a non linear fashion and presenting feedbacks, which typically are characteristics of complex systems. This kind of systems are very difficult to model in a top-down approach, due to the high number of variables in question and the nature of relations between them. The spatial dimension itself, also introduces some complexity which is difficult to capture in a deterministic approach. In this study, we propose a bottom-up approach for modelling the coastline evolution, integrating land use cover. The system is based in a probabilistic Cellular Automata (CA) model, which divides the study area in a grid. The hermetic structure of CA is overcomed by using transition rules whose weights have been calibrated by a Artificial Neural Network (ANN). In this way, the knowledge of past events is incorporated into the model and projected into the future, by means of using ”intelligence”; this contrasts with techniques such as linear regression, whose efficacy in the case study was evaluated to be much inferior. Finally, is important to mention that the use of a Geographical information Systems (GIS) environment, enabled to assemble together different types of data and overlap them in an efficient way, which would be very hard or impossible to do otherwise; therefore, we believe that the use of GIS is crucial in spatial based simulations. The case study for this model was an area of the Municipality of Almada (Portugal) that has an extensive coastal line, both Atlantic and estuary. The modelling of spatially dynamic and naturally complex phenomena occurring in these coastal areas, is important for the definition of an innovative strategy for their physical planning and also their environmental management.
dirty 0
eu_rights_str_mv openAccess
format bookPart
fulltext.url.fl_str_mv https://repositorio.ulisboa.pt/bitstreams/11086a35-0d73-4cfd-97f5-ed976612cc9d/download
id ul_e6fde60c331a30dac4c08e829e9dfe2b
identifier.url.fl_str_mv http://hdl.handle.net/10451/57840
instacron_str ul
institution Universidade de Lisboa
instname_str Universidade de Lisboa
language eng
network_acronym_str ul
network_name_str Repositório da Universidade de Lisboa
oai_identifier_str oai:repositorio.ulisboa.pt:10451/57840
organization_str_mv urn:organizationAcronym:ul
person_str_mv Simões, Joana
Rocha, Jorge
Rocha, Jorge
https://www.ciencia-id.pt/EC15-76DC-9B96
EC15-76DC-9B96
http://orcid.org/0000-0002-7228-6330
0000-0002-7228-6330
Ferreira, José Carlos
Tenedório, José António
Morgado, Paulo
Morgado, Paulo
https://www.ciencia-id.pt/6510-4FB9-6261
6510-4FB9-6261
http://orcid.org/0000-0002-3220-4943
0000-0002-3220-4943
publishDate 2009
publisher.none.fl_str_mv CoastGIS 2009
reponame_str Repositório da Universidade de Lisboa
repository_id_str urn:repositoryAcronym:ul
service_str_mv urn:repositoryAcronym:ul
spelling engCoastGIS 2009pt_PTCoastal areas stand in the intersection of human and physical factors, some of them interacting in a non linear fashion and presenting feedbacks, which typically are characteristics of complex systems. This kind of systems are very difficult to model in a top-down approach, due to the high number of variables in question and the nature of relations between them. The spatial dimension itself, also introduces some complexity which is difficult to capture in a deterministic approach. In this study, we propose a bottom-up approach for modelling the coastline evolution, integrating land use cover. The system is based in a probabilistic Cellular Automata (CA) model, which divides the study area in a grid. The hermetic structure of CA is overcomed by using transition rules whose weights have been calibrated by a Artificial Neural Network (ANN). In this way, the knowledge of past events is incorporated into the model and projected into the future, by means of using ”intelligence”; this contrasts with techniques such as linear regression, whose efficacy in the case study was evaluated to be much inferior. Finally, is important to mention that the use of a Geographical information Systems (GIS) environment, enabled to assemble together different types of data and overlap them in an efficient way, which would be very hard or impossible to do otherwise; therefore, we believe that the use of GIS is crucial in spatial based simulations. The case study for this model was an area of the Municipality of Almada (Portugal) that has an extensive coastal line, both Atlantic and estuary. The modelling of spatially dynamic and naturally complex phenomena occurring in these coastal areas, is important for the definition of an innovative strategy for their physical planning and also their environmental management.application/pdfpt_PTA bottom up approach to the modelling of costal and land use evolution throught GISSimões, JoanaPersonalRocha, JorgeDSpacehttp://dspace.org/items/9c7dabc1-d6c6-4636-9293-6babe2ba64c9DSpacehttp://dspace.org/items/9c7dabc1-d6c6-4636-9293-6babe2ba64c9RochaJorgeCiência IDhttps://www.ciencia-id.ptEC15-76DC-9B96ORCIDhttp://orcid.org0000-0002-7228-6330Researcher IDhttps://www.researcherid.comF-3185-2017Researcher IDhttps://www.researcherid.comF-3185-2017Scopus Author IDhttps://www.scopus.com56428061000Ferreira, José CarlosTenedório, José AntónioPersonalMorgado, PauloDSpacehttp://dspace.org/items/12d81dbb-2bdd-4de0-bb04-7118e50cee36DSpacehttp://dspace.org/items/12d81dbb-2bdd-4de0-bb04-7118e50cee36MorgadoPauloCiência IDhttps://www.ciencia-id.pt6510-4FB9-6261ORCIDhttp://orcid.org0000-0002-3220-4943Researcher IDhttps://www.researcherid.comJ-9673-2012Scopus Author IDhttps://www.scopus.com36741880600HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.pt2023-06-02T07:42:12Z2009-092009-09-01T00:00:00ZHandlehttp://hdl.handle.net/10451/57840http://purl.org/coar/access_right/c_abf2open accessGISArtificial Neural NetworksPlanningCellular AutomataCoastal Areas3469940 bytesliteraturehttp://purl.org/coar/resource_type/c_3248book parthttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/11086a35-0d73-4cfd-97f5-ed976612cc9d/download9th International Symposium on GIS and Computer Mapping for Coastal Management112Santa Catarina, Brazil
spellingShingle A bottom up approach to the modelling of costal and land use evolution throught GIS
A bottom up approach to the modelling of costal and land use evolution throught GIS
Simões, Joana
GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
Simões, Joana
GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
status SINGLETON
subject.fl_str_mv GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
title A bottom up approach to the modelling of costal and land use evolution throught GIS
title_full A bottom up approach to the modelling of costal and land use evolution throught GIS
title_fullStr A bottom up approach to the modelling of costal and land use evolution throught GIS
A bottom up approach to the modelling of costal and land use evolution throught GIS
title_full_unstemmed A bottom up approach to the modelling of costal and land use evolution throught GIS
A bottom up approach to the modelling of costal and land use evolution throught GIS
title_short A bottom up approach to the modelling of costal and land use evolution throught GIS
title_sort A bottom up approach to the modelling of costal and land use evolution throught GIS
topic GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
topic_facet GIS
Artificial Neural Networks
Planning
Cellular Automata
Coastal Areas
url http://hdl.handle.net/10451/57840
visible 1