Pattern-based prediction of population outbreaks.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155768 https://doi.org/10.1016/j.ecoinf.2023.102220 |
Resumo: | Resumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance associated with higher true-positive rates in most comparisons while providing interpretability rather than being a black-box method. It is an improvement over current state-of-the-art machine learning tools, especially by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide the implemented PBP method in Python through the pypbp package. |
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Pattern-based prediction of population outbreaks.Monitoramento de pragasAlert zone procedureDeep learningMachine learningTime seriesSistemas de Suporte à Tomada de DecisãoSistemas alertaAprendizado de máquinaSéries TemporaisTrigoLavouraPraga de PlantaDinâmica PopulacionalAfídeoEpidemiologiaPopulation dynamicsTime series analysisWheatResumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance associated with higher true-positive rates in most comparisons while providing interpretability rather than being a black-box method. It is an improvement over current state-of-the-art machine learning tools, especially by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide the implemented PBP method in Python through the pypbp package.GABRIEL R. PALMA, Maynooth University; WESLEY A. C. GODOY, Universidade de São Paulo; EDUARDO ENGEL, Universidade de São Paulo; DOUGLAS LAU, CNPT; EDGAR GALVAN, Maynooth University; OLIVER MASON, Maynooth University; CHARLES MARKHAM, Maynooth University; RAFAEL A. MORAL, Maynooth University.PALMA, G. R.GODOY, W. A. C.ENGEL, E.LAU, D.GALVAN, E.MASON, O.MARKHAM, C.MORAL, R. A.2023-08-08T19:24:21Z2023-08-08T19:24:21Z2023-08-082023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleEcological Informatics, v. 77, 102220, nov. 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155768https://doi.org/10.1016/j.ecoinf.2023.102220enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-08-08T19:24:21Zoai:www.alice.cnptia.embrapa.br:doc/1155768Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-08-08T19:24:21falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-08-08T19:24:21Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Pattern-based prediction of population outbreaks. |
title |
Pattern-based prediction of population outbreaks. |
spellingShingle |
Pattern-based prediction of population outbreaks. PALMA, G. R. Monitoramento de pragas Alert zone procedure Deep learning Machine learning Time series Sistemas de Suporte à Tomada de Decisão Sistemas alerta Aprendizado de máquina Séries Temporais Trigo Lavoura Praga de Planta Dinâmica Populacional Afídeo Epidemiologia Population dynamics Time series analysis Wheat |
title_short |
Pattern-based prediction of population outbreaks. |
title_full |
Pattern-based prediction of population outbreaks. |
title_fullStr |
Pattern-based prediction of population outbreaks. |
title_full_unstemmed |
Pattern-based prediction of population outbreaks. |
title_sort |
Pattern-based prediction of population outbreaks. |
author |
PALMA, G. R. |
author_facet |
PALMA, G. R. GODOY, W. A. C. ENGEL, E. LAU, D. GALVAN, E. MASON, O. MARKHAM, C. MORAL, R. A. |
author_role |
author |
author2 |
GODOY, W. A. C. ENGEL, E. LAU, D. GALVAN, E. MASON, O. MARKHAM, C. MORAL, R. A. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
GABRIEL R. PALMA, Maynooth University; WESLEY A. C. GODOY, Universidade de São Paulo; EDUARDO ENGEL, Universidade de São Paulo; DOUGLAS LAU, CNPT; EDGAR GALVAN, Maynooth University; OLIVER MASON, Maynooth University; CHARLES MARKHAM, Maynooth University; RAFAEL A. MORAL, Maynooth University. |
dc.contributor.author.fl_str_mv |
PALMA, G. R. GODOY, W. A. C. ENGEL, E. LAU, D. GALVAN, E. MASON, O. MARKHAM, C. MORAL, R. A. |
dc.subject.por.fl_str_mv |
Monitoramento de pragas Alert zone procedure Deep learning Machine learning Time series Sistemas de Suporte à Tomada de Decisão Sistemas alerta Aprendizado de máquina Séries Temporais Trigo Lavoura Praga de Planta Dinâmica Populacional Afídeo Epidemiologia Population dynamics Time series analysis Wheat |
topic |
Monitoramento de pragas Alert zone procedure Deep learning Machine learning Time series Sistemas de Suporte à Tomada de Decisão Sistemas alerta Aprendizado de máquina Séries Temporais Trigo Lavoura Praga de Planta Dinâmica Populacional Afídeo Epidemiologia Population dynamics Time series analysis Wheat |
description |
Resumo: A complexidade e a importância prática dos surtos de insetos tornaram o problema de prever surtos um foco de pesquisa recente. Propomos o método de Previsão Baseada em Padrões (PBP) para prever surtos populacionais. Este método usa informações sobre valores de séries temporais anteriores que precedem um evento de surto como preditores de surtos futuros, o que pode ser útil ao monitorar espécies de pragas. Nós ilustramos o método usando conjuntos de dados simulados e uma série temporal de pulgões obtida em lavouras de trigo no sul do Brasil. Abstract: The complexity and practical importance of insect outbreaks have made the problem of predicting outbreaks a focus of recent research. We propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be helpful when monitoring pest species. We illustrate the methodology using simulated datasets and an aphid time series obtained in wheat crops in Southern Brazil. We obtained an average test accuracy of 84.6% in the simulation studies implemented with stochastic models and 95.0% for predicting outbreaks using a time series of aphids in wheat crops in Southern Brazil. Our results show the PBP method's feasibility in predicting population outbreaks. We benchmarked our results against established state-of-the-art machine learning methods: Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance associated with higher true-positive rates in most comparisons while providing interpretability rather than being a black-box method. It is an improvement over current state-of-the-art machine learning tools, especially by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide the implemented PBP method in Python through the pypbp package. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-08-08T19:24:21Z 2023-08-08T19:24:21Z 2023-08-08 2023 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Ecological Informatics, v. 77, 102220, nov. 2023. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155768 https://doi.org/10.1016/j.ecoinf.2023.102220 |
identifier_str_mv |
Ecological Informatics, v. 77, 102220, nov. 2023. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155768 https://doi.org/10.1016/j.ecoinf.2023.102220 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503547958591488 |