@Article{RuivoCampOlivRamo:2015:AnExPr,
author = "Ruivo, Heloisa Musetti and Campos Velho, Haroldo Fraga de and
Oliveira, Gilvan Sampaio de and Ramos, Fernando Manuel",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Analysis of Extreme Precipitation Events Using a Novel Data Mining
Approach",
journal = "American Journal of Environmental Engineering",
year = "2015",
volume = "5",
number = "1A",
pages = "96--105",
keywords = "Extreme event, Drought, Intense rainfall, KDD (Knowledge Discovery
in Databases), Data mining, Classification, Decision tree.",
abstract = "An innovative data mining approach is presented and applied to
investigate the climatic causes of extreme climatic events. Our
approach comprises two main steps of knowledge extraction, applied
successively in order to reduce the complexity of the original
data set. The goal is to identify a much smaller subset of
climatic variables that might still be able to describe or even
predict the extreme events. The first step applies a class
comparison technique. The second step consists of a decision tree
learning algorithm used as a predictive model to map the set of
statistically most significant climate variables identified in the
previous step to classes of precipitation intensity. The
methodology is employed to the study the climatic causes of two
extreme events occurred in Brazil the last decade: the Santa
Catarina 2008 extreme rainfall tragedy and the Amazon droughts of
2005 and 2010. In both cases, our results are in good agreement
with analyses published in the literature.",
doi = "10.5923/s.ajee.201501.13",
url = "http://dx.doi.org/10.5923/s.ajee.201501.13",
issn = "2166-4633 and 2166-465X",
language = "en",
targetfile = "ruivo_analysis.pdf",
urlaccessdate = "11 maio 2024"
}