@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 = "27 abr. 2024"
}