Deep learning for supervised classification of temporal data in ecology

Bibliographic Details
Main Author: Capinha, César
Publication Date: 2021
Other Authors: Ceia-Hasse, Ana, Kramer, Andrew M., Meijer, Christiaan
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/180861
Summary: Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Funda??o para a Ci?ncia e a Tecnologia [CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020; ACH: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013]. Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia [CC: CEECIND/02037/2017 , UIDB/00295/2020 and UIDP/00295/2020 ; ACH: PTDC/SAU-PUB/30089/2017 and GHTM- UID/Multi/04413/2013 ]. Publisher Copyright: © 2021 The Authors
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spelling Deep learning for supervised classification of temporal data in ecologyDeep learningEcological predictionScalabilitySequential dataTemporal ecologyTime seriesEcology, Evolution, Behavior and SystematicsEcologyModelling and SimulationEcological ModellingComputer Science ApplicationsComputational Theory and MathematicsApplied MathematicsFunding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Funda??o para a Ci?ncia e a Tecnologia [CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020; ACH: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013]. Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia [CC: CEECIND/02037/2017 , UIDB/00295/2020 and UIDP/00295/2020 ; ACH: PTDC/SAU-PUB/30089/2017 and GHTM- UID/Multi/04413/2013 ]. Publisher Copyright: © 2021 The AuthorsTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.Instituto de Higiene e Medicina Tropical (IHMT)Global Health and Tropical Medicine (GHTM)RUNCapinha, CésarCeia-Hasse, AnaKramer, Andrew M.Meijer, Christiaan2025-03-18T21:12:22Z2021-032021-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9application/pdfhttp://hdl.handle.net/10362/180861eng1574-9541PURE: 33835746https://doi.org/10.1016/j.ecoinf.2021.101252info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-31T02:01:52Zoai:run.unl.pt:10362/180861Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T04:42:07.225627Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Deep learning for supervised classification of temporal data in ecology
title Deep learning for supervised classification of temporal data in ecology
spellingShingle Deep learning for supervised classification of temporal data in ecology
Capinha, César
Deep learning
Ecological prediction
Scalability
Sequential data
Temporal ecology
Time series
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
title_short Deep learning for supervised classification of temporal data in ecology
title_full Deep learning for supervised classification of temporal data in ecology
title_fullStr Deep learning for supervised classification of temporal data in ecology
title_full_unstemmed Deep learning for supervised classification of temporal data in ecology
title_sort Deep learning for supervised classification of temporal data in ecology
author Capinha, César
author_facet Capinha, César
Ceia-Hasse, Ana
Kramer, Andrew M.
Meijer, Christiaan
author_role author
author2 Ceia-Hasse, Ana
Kramer, Andrew M.
Meijer, Christiaan
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto de Higiene e Medicina Tropical (IHMT)
Global Health and Tropical Medicine (GHTM)
RUN
dc.contributor.author.fl_str_mv Capinha, César
Ceia-Hasse, Ana
Kramer, Andrew M.
Meijer, Christiaan
dc.subject.por.fl_str_mv Deep learning
Ecological prediction
Scalability
Sequential data
Temporal ecology
Time series
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
topic Deep learning
Ecological prediction
Scalability
Sequential data
Temporal ecology
Time series
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
description Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Funda??o para a Ci?ncia e a Tecnologia [CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020; ACH: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013]. Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia [CC: CEECIND/02037/2017 , UIDB/00295/2020 and UIDP/00295/2020 ; ACH: PTDC/SAU-PUB/30089/2017 and GHTM- UID/Multi/04413/2013 ]. Publisher Copyright: © 2021 The Authors
publishDate 2021
dc.date.none.fl_str_mv 2021-03
2021-03-01T00:00:00Z
2025-03-18T21:12:22Z
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/10362/180861
url http://hdl.handle.net/10362/180861
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1574-9541
PURE: 33835746
https://doi.org/10.1016/j.ecoinf.2021.101252
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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