@Article{AnochiCampHern:2019:ClPrPr,
               author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de and 
                         Hern{\'a}ndez Torres, Reynier",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Climate precipitation prediction using optimal neural network 
                         architecture in Southeast Region of Brazil",
              journal = "Journal of Computational Interdisciplinary Sciences",
                 year = "2019",
               volume = "10",
               number = "2",
                pages = "69--80",
             keywords = "metaheuristic, optimization problem, neural networks, climate 
                         prediction, mono-objective problem, multiobjective problem.",
             abstract = "Neural network is a technique successfully employed in many 
                         applications on several research fields. Despite the potential of 
                         a neural network model, its performance is dependent on the 
                         definition of the parameters, since the definition of architecture 
                         (topology) can significantly influence the training process. Here, 
                         a technique for automatic configuration for a neural network is 
                         described as an optimization problem combining two different 
                         optimization schemes: a mono-objective minimization problem using 
                         Multi-Particle Collision Algorithm (MPCA), and a multiobjective 
                         minimization problem Nondominated Sorting Genetic Algorithm 
                         (NSGA-II). The proposed optimization approaches were tested for 
                         the mesoscale seasonal climate prediction for precipitation. The 
                         meteorological data were processed by Rough Set Theory to extract 
                         relevant information to perform the climate prediction by neural 
                         network for the Southeast region of Brazil, with a reduced data 
                         set.",
                 issn = "1983-8409 and 2177-8833",
             language = "en",
           targetfile = "anochi_climate.pdf",
        urlaccessdate = "20 abr. 2024"
}