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@Article{DebastianiRafaDala:2019:ÁrMoFr,
               author = "Debastiani, Aline Bernarda and Rafaeli Neto, Silvio Lu{\'{\i}}s 
                         and Dalagnol, Ricardo",
          affiliation = "{Universidade do Estado de Santa Catarina (UESC)} and 
                         {Universidade do Estado de Santa Catarina (UESC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "{\'A}rvore modelo frente a uma rede neural artificial para a 
                         modelagem chuva-vaz{\~a}o",
              journal = "Nativa",
                 year = "2019",
               volume = "7",
               number = "5",
                pages = "527--534",
                month = "Sept./Oct.",
             keywords = "artificial neural network, regression tree, Basin Alto Canoas.",
             abstract = "The aim of this study is to investigate the performance of the 
                         model tree (M5P) and its sensitivity to pruning and comparison to 
                         the performance of an Artificial Neural network (ANN) for the 
                         simulation of daily average discharge of the month. The motivation 
                         for this analysis is on simplicity and speed of processing M5P 
                         compared the RNAs. The study was developed in the Alto Canoas 
                         watershed, having an experiment consisting of a training period, a 
                         cross-validation and two testing periods. The ANN used was the 
                         Multi Layer Perceptron (MLP), implemented in MATLAB software, and 
                         M5P (with and without pruning), available from the WEKA software. 
                         M5P algorithm proved sensitive to pruning in half of the 
                         treatments. The M5P showed good fit in the modeling, but the RNA 
                         presented superior performance in all treatments.",
                  doi = "10.31413/nativa.v7i5.7089",
                  url = "http://dx.doi.org/10.31413/nativa.v7i5.7089",
                 issn = "2318-7670",
             language = "en",
           targetfile = "7089-31774-1-PB.pdf",
        urlaccessdate = "12 abr. 2021"
}


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