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dc.contributor.authorPALMA, G. R.
dc.contributor.authorGODOY, W. A. C.
dc.contributor.authorENGEL, E.
dc.contributor.authorLAU, D.
dc.contributor.authorGALVAN, E.
dc.contributor.authorMASON, O.
dc.contributor.authorMARKHAM, C.
dc.contributor.authorMORAL, R. A.
dc.date.accessioned2023-08-08T19:24:21Z-
dc.date.available2023-08-08T19:24:21Z-
dc.date.created2023-08-08
dc.date.issued2023
dc.identifier.citationEcological Informatics, v. 77, 102220, nov. 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1155768-
dc.descriptionResumo: 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectMonitoramento de pragas
dc.subjectAlert zone procedure
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectTime series
dc.subjectSistemas de Suporte à Tomada de Decisão
dc.subjectSistemas alerta
dc.subjectAprendizado de máquina
dc.subjectSéries Temporais
dc.titlePattern-based prediction of population outbreaks.
dc.typeArtigo de periódico
dc.subject.thesagroTrigo
dc.subject.thesagroLavoura
dc.subject.thesagroPraga de Planta
dc.subject.thesagroDinâmica Populacional
dc.subject.thesagroAfídeo
dc.subject.thesagroEpidemiologia
dc.subject.nalthesaurusPopulation dynamics
dc.subject.nalthesaurusTime series analysis
dc.subject.nalthesaurusWheat
riaa.ainfo.id1155768
riaa.ainfo.lastupdate2023-08-08
dc.identifier.doihttps://doi.org/10.1016/j.ecoinf.2023.102220
dc.contributor.institutionGABRIEL 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.
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