@InProceedings{WeigangSaNord:1996:NeNePr,
               author = "Weigang, Li and Sa, Leonardo Deane de Abreu and Nordemann, Daniel 
                         J. R.",
          affiliation = "{CPTEC-INPE-Cachoeira Paulista-12630-000-SP-Brasil}",
                title = "Neural networks for prediction of the sea surface temperature 
                         (Sst) in the tropical ocean",
            booktitle = "Anais...",
                 year = "1996",
                pages = "755--758",
         organization = "Congresso Brasileiro de Meteorologia, 9.",
             keywords = "temperatura da superficie do mar, previsao, El nino, Enso, redes 
                         neurais.",
             abstract = "A brief review of researches on the application of the neural 
                         networks in the area of meteorology, oceanography and 
                         geographysics is introduced. The method of Neural Nelworks as one 
                         valuable non-linear strategies to reconstruct and predict 
                         climatologival signals is aIs o reviewed. Feedforward Neural 
                         Networks are implemenled in a neural network simulator SNNS for 
                         prediction of the Sea Surface Temperature (SST) in lhe tropical 
                         Pacific oceano The original SST is 'collected from the NINO1-2 (00 
                         N-1 00 S, 2700 E-2800 E) and NINO4 (50 N-5° S, 1600 E-1500 E) 
                         regions from January 1950 to now. Using the available data to 
                         train the network, the network then provides the next six 
                         monthprediction. Comparing with the corresponding six month 
                         observations, ali prediction values are located within the 
                         predicted errar bars. The errar detected have shown that the 
                         neural networks method is a uasful tool to perform cJimatological 
                         predictions.",
  conference-location = "Campos do Jord{\~a}o",
      conference-year = "6-13 nov.",
           copyholder = "SID/SCD",
             language = "en",
         organisation = "SBMET",
           targetfile = "11126.pdf",
               volume = "1",
        urlaccessdate = "05 maio 2024"
}