@Article{RuivoCampRamoFrei:2018:DaMiFl,
author = "Ruivo, Heloisa Musetti and Campos Velho, Haroldo Fraga de and
Ramos, Fernando Manuel and Freitas, Saulo Ribeiro de",
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 = "Data Mining for Flooding Episode in the States of Alagoas and
Pernambuco—Brazil",
journal = "American Journal of Climate Change",
year = "2018",
volume = "7",
number = "3",
pages = "420--130",
keywords = "Data Mining, Statistical Analysis, T-Test, p-Value, Artificial
Intelligence, Decision Tree.",
abstract = "The increasing volume of data in the area of environmental
sciences needs analysis and interpretation. Among the challenges
generated by this data deluge, the development of efficient
strategies for the knowledge discovery is an important issue.
Here, statistical and tools from computational intelligence are
applied to analyze large data sets from meteorology and climate
sciences. Our approach allows a geographical mapping of the
statistical property to be easily interpreted by meteorologists.
Our data analysis comprises two main steps of knowledge
extraction, applied successively in order to reduce the complexity
from 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 probability of occurrence of an extreme event.
The first step applies a class comparison technique: p-value
estimation. The second step consists of a decision tree (DT)
configured from the data available and the p-value analysis. The
DT is used as a predictive model, identifying the most
statistically significant climate variables of the precipitation
intensity. The methodology is employed to the study the climatic
causes of an extreme precipitation events occurred in Alagoas and
Pernambuco States (Brazil) at June/2010.",
doi = "10.4236/ajcc.2018.73025",
url = "http://dx.doi.org/10.4236/ajcc.2018.73025",
issn = "2167-9495 and 2167-9509",
language = "en",
targetfile = "ruivo_data.pdf",
urlaccessdate = "27 abr. 2024"
}