Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.

Detalhes bibliográficos
Autor(a) principal: Bieleveld, Michel Jan Marinus
Data de Publicação: 2016
Tipo de documento: Tese
Idioma: eng
Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
Texto Completo: http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/
Resumo: Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30% of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.
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spelling Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.Melhorar a qualidade de modelo de distribuição das espécies com um algoritmo paralelo de programação linear genético-fuzzy.Algoritmos genéticosApplied and specific algorithmsBioclimatologiaEcological niche modelsFuzzy logicGenetic algorithmsInteligência artificialLógica FuzzySpecies distribution modellingBiodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30% of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.Biodiversidade, a variedade de vida no planeta, está em declínio às alterações climáticas, mudanças nas interações das populações e espécies, bem como nas alterações demográficas e na dinâmica de paisagens. Avaliações integradas baseadas em modelo desempenham um papel fundamental na compreensão e na exploração destas dinâmicas complexas e tem o seu uso comprovado no planejamento de conservação da biodiversidade. Os objetivos deste estudo de doutorado foram investigar; (1) o uso de técnicas de programação genética e fuzzy para construir modelos de alta qualidade que lida com presença e ausência de dados ruidosos do mundo real, (2) a extensão desta solução para explorar o paralelismo inerente à programação genética para acelerar tomadas de decisão e (3) um framework conceitual para compartilhar modelos, na expectativa de permitir a síntese de pesquisa. Subsequentemente, a qualidade do método, avaliada com a true skill statistic, foi examinado com dois estudos de caso. O primeiro utilizou um conjunto de dados fictícios obtidos a partir da definição de uma espécie virtual, e o segundo utilizou dados de uma espécie de pomba (Zenaida macroura) obtidos do North American Breeding Bird Survey. Nestes estudos, os modelos foram capazes de predizer a distribuição das espécies maneira correta mesmo utilizando bases de dados com até 30% de erros nas amostras de presença e de ausência. A implementação paralela utilizando um cluster de vinte nós c3.xlarge Amazon EC2 StarCluster, mostrou uma aceleração linear devido ao arquitetura de múltiplos deme de granulação grossa. O algoritmo de programação genética e fuzzy gerada em determinadas condições durante os estudos de caso, foram significativamente melhores na transferência do que os algoritmos do BIOMOD.Biblioteca Digitais de Teses e Dissertações da USPSaraiva, Antonio MauroBieleveld, Michel Jan Marinus2016-09-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2024-10-09T12:51:22Zoai:teses.usp.br:tde-26012017-113329Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212024-10-09T12:51:22Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
Melhorar a qualidade de modelo de distribuição das espécies com um algoritmo paralelo de programação linear genético-fuzzy.
title Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
spellingShingle Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
Bieleveld, Michel Jan Marinus
Algoritmos genéticos
Applied and specific algorithms
Bioclimatologia
Ecological niche models
Fuzzy logic
Genetic algorithms
Inteligência artificial
Lógica Fuzzy
Species distribution modelling
title_short Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
title_full Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
title_fullStr Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
title_full_unstemmed Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
title_sort Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm.
author Bieleveld, Michel Jan Marinus
author_facet Bieleveld, Michel Jan Marinus
author_role author
dc.contributor.none.fl_str_mv Saraiva, Antonio Mauro
dc.contributor.author.fl_str_mv Bieleveld, Michel Jan Marinus
dc.subject.por.fl_str_mv Algoritmos genéticos
Applied and specific algorithms
Bioclimatologia
Ecological niche models
Fuzzy logic
Genetic algorithms
Inteligência artificial
Lógica Fuzzy
Species distribution modelling
topic Algoritmos genéticos
Applied and specific algorithms
Bioclimatologia
Ecological niche models
Fuzzy logic
Genetic algorithms
Inteligência artificial
Lógica Fuzzy
Species distribution modelling
description Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30% of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.
publishDate 2016
dc.date.none.fl_str_mv 2016-09-09
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
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dc.identifier.uri.fl_str_mv http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/
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dc.language.iso.fl_str_mv eng
language eng
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dc.rights.driver.fl_str_mv Liberar o conteúdo para acesso público.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Liberar o conteúdo para acesso público.
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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dc.publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
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reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
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institution USP
reponame_str Biblioteca Digital de Teses e Dissertações da USP
collection Biblioteca Digital de Teses e Dissertações da USP
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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