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      Modelagem preditiva de linha de costa utilizando redes neurais artificiais Translated title: Shoreline predictive modeling using artificial neural networks

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          Abstract

          Estudar modelagens através de dados geodésicos temporais com a possibilidade de predizer a posição de linha de costa é uma tarefa importante e pode auxiliar significativamente na gestão costeira. A área de estudo neste trabalho se refere ao município de Matinhos no estado do Paraná, Brasil. As linhas de costa temporais utilizadas para testar a modelagem preditiva são provenientes respectivamente da fotogrametria analógica para anos 1954, 1963, 1980, 1991 e 1997 e de levantamentos geodésicos utilizando GPS (Global Position System) para 2001, 2002, 2005 e 2008 (como controle). Dois testes com as redes neurais artificiais foram organizados mudando alguns parâmetros como: arquitetura, número de neurônios nas camadas ocultas e algoritmos de treinamentos. Quando comparados o valor dos resíduos entre a predição e a linha de costa de controle, os melhores resultados estatísticos indicam que o MAPE (mean absolute percentage error) são 0,28% utilizando a rede neural parcialmente recorrente de Elman com o algoritmo de treinamento quase-Newton e 0,46% para o caso da rede neural perceptron multicamadas com o algoritmo de treinamento utilizando o método Bayesiano com regularização.

          Translated abstract

          The study of models using geodetic temporal data which can possibly predict the shoreline position is an important task and can significantly contribute to coastal management. The studied area is located at municipality of Matinhos in the Paraná State, Brazil. The temporal shoreline used to test the prediction model is respectively from analog photogrammetric data, related to the years 1954, 1963, 1980, 1991 and 1997, and GPS (Global Position System) geodetic surveys for 2001, 2002, 2005 and 2008 (as control). Two different tests with artificial neural network were organized setting the parameters like: architecture, number of neuron in hidden layers and the training algorithms. Comparing the residuals between the prediction to the shoreline of control, the best statistical results show the MAPE (Mean Absolute Percentage Error) is 0,28% using the Elman partially recurrent network with quasi-Newton training function and 0,46% using the neural network multilayer perceptron with Bayesian regulation training function.

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          Most cited references 26

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          Bayesian Interpolation

           David MacKay (1992)
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            Conditioning of quasi-Newton methods for function minimization

             D. F. Shanno (1970)
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              A family of variable-metric methods derived by variational means

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                bcg
                Boletim de Ciências Geodésicas
                Bol. Ciênc. Geod.
                Universidade Federal do Paraná (Curitiba )
                1982-2170
                September 2010
                : 16
                : 3
                : 420-444
                Affiliations
                [1 ] Universidade Federal de Pernambuco Brazil
                [2 ] Universidade Federal do Paraná Brazil
                [3 ] Pontifícia Universidade Católica do Paraná Brazil
                [4 ] Karlsruhe Institute of Technology Germany
                Article
                S1982-21702010000300004
                10.1590/S1982-21702010000300004
                Product
                Product Information: website
                Categories
                GEOCHEMISTRY & GEOPHYSICS
                REMOTE SENSING

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