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• W3107789206 abstract "ABSTRACT. This study aimed to analyze 70 years historical series in the Brazilian Southeastern region, using monthly rainfall data. Statistical modeling techniques such as cross-wavelet spectra and artificial neural networks (ANN), from the R statistical package, were used to perform the analyses. Two different types of neural networks were employed: the multi-layer perceptron (MLP) and extreme learning machine (ELM). From the cited time series, the analysis shows the existence of a decadal and multi-decadal signal with cycles of 5, 11, and 22 years in the monthly rainfall in Brazilian Southeastern region, observing the existence of low-frequency variability. This shows a significant degree of modulation and association for the precipitation with solar activity. The neural networks were also used as forecasting tools, with a better performance for MLP-NN – smaller root mean square error. However, the MLP-NN presented a greater confidence interval than ELM-NN.Keywords: monthly rainfall, sunspots, multi-decadal cycles.Observando a Existência de Variabilidade de Baixa Frequência em Dados Mensais de Chuva no Sudeste do Brasil utilizando Ferramentas do Aplicativo R - Redes Neurais e OndeletasRESUMO. Este estudo teve como objetivo analisar séries históricas de 70 anos no sudeste do Brasil, utilizando dados mensais de precipitação. Técnicas de análise estatística usando o pacote estatístico R, como espectros de wavelet cruzado e modelagem de redes neurais artificiais (RNA), foram usadas para realizar as análises. Duas implementações de redes neurais foram empregadas: multi-layer perceptron (MLP) e extreme learning machine (ELM). Os resultados obtidos nas análises realizadas permitem inferir que as séries temporais observadas mostram a existência de um sinal decenal e multi-decenal com ciclos de 5, 11 e 22 anos na precipitação mensal no sudeste do Brasil, observando a existência de variabilidade de baixa frequência nos dados analisados. Isso mostra um grau significativo de modulação e associação da precipitação com a atividade solar. A análise de séries temporais longas permitem a observação de variabilidades de baixa frequência, evidenciando sua grande importância e relevância. Uma significativa parcela da variância total de ciclos atmosféricos decenais é modulado pela atividade solar. As redes neurais também foram usadas como ferramentas de previsão, com melhor desempenho para a rede MLP – como mostrado pelo erro médio quadrático. A rede MLP apresentou maior amplitude no intervalor de confiança do que a rede ELM.Palavras-chave: precipitação mensal, manchas solares, ciclos multi-decenais." @default.
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• W3107789206 date "2020-06-23" @default.
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• W3107789206 title "Observing the Existence of Low-Frequency Variability in Monthly Rainfall Data at Southeastern Brazil using R Package Tools – Neural Networks and Wavelet" @default.
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• W3107789206 doi "https://doi.org/10.22564/rbgf.v38i2.2046" @default.
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