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@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 nov. 2020"
}


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