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@Article{AnochiCamp:2016:PrClPr,
               author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Previs{\~a}o clim{\'a}tica de precipita{\c{c}}{\~a}o para a 
                         regi{\~a}o Sul por rede neural autoconfigurada",
              journal = "Ci{\^e}ncia e Natura",
                 year = "2016",
               volume = "38",
               number = "Esp.",
                pages = "98--104",
                 note = "IX Workshop Brasileiro de Micrometeorologia, 2016.",
             keywords = "Previs{\~a}o clim{\'a}tica, precipita{\c{c}}{\~a}o, rede 
                         neural, otimiza{\c{c}}{\~a}o, redu{\c{c}}{\~a}o de dados, 
                         Climate prediction, precipitation, neural network, optimazation, 
                         data reduction.",
             abstract = "Previs{\~a}o clim{\'a}tica do campo de precipita{\c{c}}{\~a}o 
                         {\'e} um aspecto chave, pois esta {\'e} uma vari{\'a}vel 
                         meteorol{\'o}gica dif{\'{\i}}cil, de grande variabilidade 
                         temporal e espacial, com forte impacto para a sociedade. Um 
                         m{\'e}todo baseado em rede neural artificial {\'e} aplicado para 
                         previs{\~a}o mensal e sazonal de precipita{\c{c}}{\~a}o na 
                         regi{\~a}o Sul do Brasil. O uso de redes neurais como um modelo 
                         preditivo {\'e} bastante difundido em diferentes 
                         aplica{\c{c}}{\~o}es. A melhor configura{\c{c}}{\~a}o para a 
                         rede neural {\'e} calculada automaticamente. O esquema de 
                         autoconfigura{\c{c}}{\~a}o {\'e} descrito como um problema de 
                         otimiza{\c{c}}{\~a}o. ABSTRACT: Climate prediction for 
                         precipitation field is a key issue, because such meteorological 
                         variable is the challenge for climate and weather forecasting due 
                         to the high spatial and temporal variability with strong impact on 
                         the society. A method based on the artificial neural network is 
                         applied to monthly and seasonal precipitation forecast in southern 
                         Brazil. The use of neural networks as a predictive model is 
                         widespread in different applications. The best configuration for 
                         the neural network is automatically calculated. The 
                         autoconfiguration scheme is described as an optimization 
                         problem.",
                  doi = "10.5902/2179460X19968",
                  url = "http://dx.doi.org/10.5902/2179460X19968",
                 issn = "0100-8307 and 2179-460X",
             language = "pt",
           targetfile = "Anochi_Previsao.pdf",
                  url = "http://periodicos.ufsm.br/cienciaenatura/article/view/19968/pdf",
        urlaccessdate = "04 dez. 2020"
}


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