@InProceedings{LiSaNordBevi:1995:PrSeSu,
               author = "Li, W. G. and Sa, Leonardo Deane de Abreu and Nordemann, Daniel 
                         Jean Roger and Bevilaqua, Rute Maria",
                title = "Predictions of sea surface temperature in Tropical Atlantic Ocean 
                         time series using neural networks",
            booktitle = "Anais...",
                 year = "1995",
                pages = "221--222",
         organization = "Conferencia Regional sobre Mudancas Globais.",
             keywords = "ESTUDO DO SINAL GEOFISICO, OCEANO ATLANTICO, REDES NEURAIS.",
             abstract = "A review of researches on the relationship between the tropical 
                         ocean sea surface temperatures (SST) and rainfall anomalies in 
                         Northeast Brazil was introduced. In this work, two neural network 
                         models are implemented to reconstruct and predict the time series 
                         of the SST in two regions: the tropical Atlantic ocean (Wright 
                         index, from 1854 to 1985) and the tropical Pacific ocean (regions 
                         Nino1-2: 0 N-10 S, 270 E-280 E and Nino4: 5 N-5 S, 160 E-150 E, 
                         from 1950 to 1995). The selected neural networks include 
                         Backpropagation Neural Network (BNN) and Time Delay Neural Network 
                         (TDNN). Both were implemented in the neural network stimulator 
                         SNNS. For the Wright index, the trained Backpropagation Neural 
                         Network successfully predicted the index of the following four 
                         months with the relative errors from 1.40 to 3.34. For SST in 
                         Nino1-2 and Nino4 regions, the Time Delay Neural Network was used 
                         for reconstruction and prediction. Comparing with the next six 
                         month observations and predictions, all of them are located within 
                         the predicted error bars. These results show that neural network 
                         methods may be used, within certain limits, for prediction and 
                         evaluation of predictability of time series measured from 
                         phenomena influenced by complex climatic and geophysical 
                         processes, like SST.",
  conference-location = "Sao Paulo, BR",
      conference-year = "04-06 dez. 1995",
                label = "7422",
           targetfile = "6084.pdf",
        urlaccessdate = "09 maio 2024"
}