@InProceedings{AnochiSilv:2011:NeNeMo,
               author = "Anochi, Juliana Aparecida and Silva, Jos{\'e} Demisio Sim{\~o}es 
                         da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Neural Network Models for Climate Forecasting based on Reanalysis 
                         Data",
            booktitle = "Proceedings...",
                 year = "2011",
         organization = "Congresso Brasileiro de Intelig{\^e}ncia Computacional, 10.",
             keywords = "Climate Forecasting, Rough Sets Theory, Artificial Neural 
                         Networks, Artificial Intelligence.",
             abstract = "In this work a neural network model for climate forecasting is 
                         presented. The model is built by training a neural network with 
                         available reanalysis data. In order to assess the model, the 
                         development methodology considers the use of data reduction 
                         strategies that eliminate data redundancy thus reducing the 
                         complexity of the models. The results presented in this paper 
                         considered the use of Rough Sets Theory principles in extracting 
                         relevant information from the available data to achieve the 
                         reduction of redundancy among the variables used for forecasting 
                         purposes. The paper presents results of climate prediction made 
                         with the use of the neural network based model.",
  conference-location = "Fortaleza, CE",
      conference-year = "8-11 nov.",
                label = "lattes: 2720072834057575 1 AnochiSilv:2011:NeNeMo",
             language = "en",
           targetfile = "anochi_neural.pdf",
                  url = "http://cbrn-cbic2011.org/",
        urlaccessdate = "11 maio 2024"
}