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@PhDThesis{Anochi:2015:PrClPr,
               author = "Anochi, Juliana Aparecida",
                title = "Previs{\~a}o clim{\'a}tica de precipita{\c{c}}{\~a}o por redes 
                         neurais autoconfiguradas",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-11-03",
             keywords = "problemas de otimiza{\c{c}}{\~a}o, meta-heur{\'{\i}}stica, 
                         rede neural artificial, previs{\~a}o clim{\'a}tica, 
                         redu{\c{c}}{\~a}o de dados, optimization, meta-heuristic, 
                         artificial neural networks, climate prediction, data reduction.",
             abstract = "Previs{\~a}o clim{\'a}tica do campo de precipita{\c{c}}{\~a}o 
                         {\'e} um aspecto chave em meteorologia. Precipita{\c{c}}{\~a}o 
                         {\'e} uma vari{\'a}vel associada a desastres naturais (secas e 
                         enchentes) e safras agr{\'{\i}}colas, com impactos nos setores 
                         de turismo e transporte. Entretanto esta vari{\'a}vel 
                         meteorol{\'o}gica {\'e} de dif{\'{\i}}cil previs{\~a}o, 
                         devido {\`a} grande variabilidade temporal e espacial 
                         (vari{\'a}vel descont{\'{\i}}nua). Neste trabalho, um 
                         m{\'e}todo baseado em Rede Neural Artificial (RNA) {\'e} 
                         aplicado para previs{\~a}o clim{\'a}tica de 
                         precipita{\c{c}}{\~a}o nas regi{\~o}es Sul, Sudeste e Nordeste 
                         do Brasil. {\'E} conhecida a capacidade de redes neurais de 
                         aprendizado e resposta, o que motiva sua aplica{\c{c}}{\~a}o com 
                         sucesso em uma grande variedade de problemas, consolidando-se como 
                         uma t{\'e}cnica de solu{\c{c}}{\~a}o de problemas complexos em 
                         reconhecimento de padr{\~o}es, classifica{\c{c}}{\~a}o, 
                         sistemas de controle, aproxima{\c{c}}{\~a}o de 
                         fun{\c{c}}{\~o}es e modelo preditivo. Redes neurais podem ser 
                         caracterizadas como redes supervisionadas e n{\~a}o 
                         supervisionadas. Em geral, o processo de treinamento de redes 
                         neurais supervisionadas est{\'a} associado {\`a} 
                         determina{\c{c}}{\~a}o dos pesos das conex{\~o}es. A 
                         defini{\c{c}}{\~a}o ou identifica{\c{c}}{\~a}o da arquitetura 
                         {\'o}tima para uma rede neural {\'e} expressa como um problema 
                         de otimiza{\c{c}}{\~a}o, em que cada ponto no espa{\c{c}}o de 
                         busca representa uma topologia diferente. O problema de 
                         otimiza{\c{c}}{\~a}o pode ser formulado por meio de uma 
                         fun{\c{c}}{\~a}o mono-objetivo ou de uma fun{\c{c}}{\~a}o 
                         multiobjetivo. Neste trabalho, a otimiza{\c{c}}{\~a}o 
                         mono-objetivo foi solucionada pelo \emph{Multi-Particle Collision 
                         Algorithm} (MPCA) e o \emph{Non-dominated Sorting Genetic 
                         Algorithm} II (NSGA-II) foi empregado para otimiza{\c{c}}{\~a}o 
                         multiobjetivo. Em meteorologia, dados de diversas fontes 
                         (sat{\'e}lites, esta{\c{c}}{\~o}es de superf{\'{\i}}cie, 
                         boias oce{\^a}nicas, radiossondagens, radar e muitas outras) 
                         s{\~a}o usados nas previs{\~o}es de tempo e clima. Assim, 
                         previs{\~a}o de eventos meteorol{\'o}gicos {\'e} um desafio 
                         complexo, mais ainda deve-se incluir a necessidade de an{\'a}lise 
                         de grande volume de dados. A redu{\c{c}}{\~a}o da dimens{\~a}o 
                         de dados de observa{\c{c}}{\~a}o sem perda de 
                         informa{\c{c}}{\~a}o {\'e} um tema importante de pesquisa. A 
                         Teoria dos Conjuntos Aproximativos, uma t{\'e}cnica de 
                         minera{\c{c}}{\~a}o de dados, foi empregada para identificar as 
                         vari{\'a}veis mais significativas para o processo de 
                         previs{\~a}o clim{\'a}tica. ABSTRACT: Climate precipitation 
                         prediction field is a key aspect in meteorology. The precipitation 
                         is a variable associated with natural disasters (droughts and 
                         floods) agricultural crops and can cause impacts in the sectors of 
                         tourism and shipping. However, this is a meteorological variable 
                         that is difficult to predict because of large spatial and temporal 
                         variability (i.e. variable discontinuous). A method based on 
                         Artificial Neural Network (ANN) is applied to climate prediction 
                         precipitation in the South, Southeast and Northeast regions of 
                         Brazil. It is known the ability of neural network learning and 
                         response, which motivates their successful application in a wide 
                         variety of problems, consolidating its position as a solution 
                         technique of complex problems in pattern recognition, 
                         classification, control systems, proximity functions and 
                         predictive model. Neural networks can be characterized as 
                         supervised and unsupervised networks. In general, the supervised 
                         training process for neural networks is associated with the 
                         determination of the weights of the connections. The definition or 
                         identification of the optimal architecture for a neural network is 
                         expressed as an optimization problem, in which each point in the 
                         search space represents a different topology. The optimization 
                         problem can be formulated by a mono-objective function or a 
                         multiobjective function. The mono-objective optimization was 
                         solved by Multi-Particle Collision Algorithm (MPCA) and 
                         Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used for 
                         multi-objective optimization. In meteorology, data from various 
                         sources (satellites, ground-based stations, ocean buoys, 
                         soundings, radar and many others) are used in weather and climate 
                         forecasts. Predicting meteorological events is a complex 
                         challenge. The size reduction of the observation data without 
                         losing information is an important subject of research. The Rough 
                         Sets Theory, a data mining technique was used to identify the most 
                         significant variables for the climate prediction process.",
            committee = "Guimar{\~a}es, Lamartine Nogueira Frutuoso (presidente) and 
                         Velho, Haroldo Fraga de Campos (orientador) and Shiguemori, Elcio 
                         Hideiti (orientador) and Sandri, Sandra Aparecida and Carvalho, 
                         Solon Ven{\^a}ncio de and Luz, Eduardo F{\'a}vero Pacheco da and 
                         Braga, Antonio de Padua",
           copyholder = "SID/SCD",
         englishtitle = "Climate precipitation prediction by self-configured neural 
                         networks",
             language = "pt",
                pages = "159",
                  ibi = "8JMKD3MGP3W34P/3K98PDP",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3K98PDP",
           targetfile = "publicacao.pdf",
        urlaccessdate = "25 nov. 2020"
}


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