Towards the use of genetic programming in the ecological modelling of mosquito population dynamics

Detalhes bibliográficos
Autor(a) principal: Azzali, Irene
Data de Publicação: 2020
Outros Autores: Vanneschi, Leonardo, Mosca, Andrea, Bertolotti, Luigi, Giacobini, Mario
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145908
Resumo: Azzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., & Giacobini, M. (2020). Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genetic Programming And Evolvable Machines. https://doi.org/10.1007/s10710-019-09374-0
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spelling Towards the use of genetic programming in the ecological modelling of mosquito population dynamicsEcological modellingGenetic programmingMachine learningRegressionSoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Science ApplicationsSDG 15 - Life on LandAzzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., & Giacobini, M. (2020). Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genetic Programming And Evolvable Machines. https://doi.org/10.1007/s10710-019-09374-0Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAzzali, IreneVanneschi, LeonardoMosca, AndreaBertolotti, LuigiGiacobini, Mario2022-11-30T22:08:40Z2020-122020-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/145908eng1389-2576PURE: 16446465https://doi.org/10.1007/s10710-019-09374-0info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:26:40Zoai:run.unl.pt:10362/145908Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:20.205154Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
title Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
spellingShingle Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
Azzali, Irene
Ecological modelling
Genetic programming
Machine learning
Regression
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
SDG 15 - Life on Land
title_short Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
title_full Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
title_fullStr Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
title_full_unstemmed Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
title_sort Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
author Azzali, Irene
author_facet Azzali, Irene
Vanneschi, Leonardo
Mosca, Andrea
Bertolotti, Luigi
Giacobini, Mario
author_role author
author2 Vanneschi, Leonardo
Mosca, Andrea
Bertolotti, Luigi
Giacobini, Mario
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Azzali, Irene
Vanneschi, Leonardo
Mosca, Andrea
Bertolotti, Luigi
Giacobini, Mario
dc.subject.por.fl_str_mv Ecological modelling
Genetic programming
Machine learning
Regression
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
SDG 15 - Life on Land
topic Ecological modelling
Genetic programming
Machine learning
Regression
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
SDG 15 - Life on Land
description Azzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., & Giacobini, M. (2020). Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genetic Programming And Evolvable Machines. https://doi.org/10.1007/s10710-019-09374-0
publishDate 2020
dc.date.none.fl_str_mv 2020-12
2020-12-01T00:00:00Z
2022-11-30T22:08:40Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145908
url http://hdl.handle.net/10362/145908
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1389-2576
PURE: 16446465
https://doi.org/10.1007/s10710-019-09374-0
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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