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@InProceedings{AnochiTorrCamp:2020:ClPrPr,
               author = "Anochi, Juliana Aparecida and Torres, Reynier Hern{\'a}ndez and 
                         Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Climate precipitation prediction with uncertainty quantification 
                         by self-configuring neural network",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Cursi, J. E. S.",
                pages = "242--253",
         organization = "International Symposium on Uncertainty Quantification and 
                         Stochastic Modelling, 5.",
            publisher = "Springer",
                 note = "{Lecture Notes in Mechanical Engineering}",
             keywords = "Neural network, Precipitation climate prediction, MPCA 
                         metaheuristic.",
             abstract = "Artificial neural networks have been employed on many 
                         applications. Good results have been obtained by using neural 
                         network for the precipitation climate prediction to the Brazil. 
                         The input are some meteorological variables, as wind components 
                         for several levels, air temperature, and former precipitation. The 
                         neural network is automatically configured, by solving an 
                         optimization problem with Multi-Particle Collision Algorithm 
                         (MPCA) metaheuristic. However, it is necessary to address, beyond 
                         the prediction the uncertainty associated to the prediction. This 
                         paper is focused on two-fold. Firstly, to produce a monthly 
                         prediction for precipitation by neural network. Secondly, the 
                         neural network output is also designed to estimate the uncertainty 
                         related to neural prediction.",
  conference-location = "Rouen, France",
      conference-year = "29 jun. - 03 jul.",
                 isbn = "978-303053668-8",
                 issn = "21954356",
             language = "en",
           targetfile = "anochi_climate.pdf",
        urlaccessdate = "12 abr. 2021"
}


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