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@InProceedings{Ruivo:2016:DaMiAp,
               author = "Ruivo, Heloisa Musetti",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Severe precipitation evaluation in Brazil: Data mining approach",
                 year = "2016",
         organization = "International Conference on Integral Methods in Science and 
                         Engineering, 14. (IMSE)",
             keywords = "Severe precipitation, statistical p-value analysis, decision tree 
                         algorithm.",
             abstract = "Data mining approach is applied to evaluate extreme rainfall 
                         events in the Brazil. Statistical analysis is combined with an 
                         artificial intelligence technique to identify the most relevant 
                         meteorological variables for a local severe precipitation in the 
                         Rio de Janeiro state (Brazil): Rio de Janeiro, and Nova Friburgo 
                         cities. The p-value statistical technique is employed to select a 
                         much smaller subset of climatic variables, preserving the 
                         information associated with extreme meteorological events. A 
                         decision tree algorithm is used as a model to identify the 
                         precipitation severity. The method is tested with the events at 
                         Apr/2009 (Rio de Janeiro city) and at Jan/2011 (Nova Friburgo 
                         city). In both cases, our results show a good local analysis for 
                         extreme precipition episodes.",
  conference-location = "Padova, Italy",
      conference-year = "25-29 July",
             language = "en",
           targetfile = "IMSE-2016_Data_mining.pdf",
        urlaccessdate = "24 nov. 2020"
}


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