Data/hora: 29/05/2024 05:50:50

Biblioteca(s): 	Embrapa Trigo.	
Data corrente: 	08/08/2023	
Data da última atualização: 	08/08/2023	
Autoria: 	PALMA, G. R.; GODOY, W. A. C.; ENGEL, E.; LAU, D.; GALVAN, E.; MASON, O.; MARKHAM, C.; MORAL, R. A.	
Afiliação: 	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.	
Título: 	Pattern-based prediction of population outbreaks.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Ecological Informatics, v. 77, 102220, nov. 2023.	
DOI: 	https://doi.org/10.1016/j.ecoinf.2023.102220	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Alert zone procedure; Aprendizado de máquina; Deep learning; Machine learning; Monitoramento de pragas; Séries Temporais; Sistemas alerta; Sistemas de Suporte à Tomada de Decisão; Time series.	
Thesagro: 	Afídeo; Dinâmica Populacional; Epidemiologia; Lavoura; Praga de Planta; Trigo.	
Thesaurus Nal: 	Population dynamics; Time series analysis; Wheat.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155768/1/Pattern-based-prediction-of-population-outbreaks.pdf	
Registro original: 	Embrapa Trigo (CNPT)	
Exemplares: 	CNPT - (45553-1) - UPC - AP - DD -  -  -  - DIS