@InProceedings{ÁvilaPintZullAssa:2003:EsPrIm,
               author = "{\'A}vila, Ana Maria Heuminski de and Pinto, Hilton Silveira and 
                         Zullo J{\'u}nior, Jurandir and Assad, Eduardo Delgado",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP). FEAGRI.} and 
                         {Universidade Estadual de Campinas (UNICAMP). Centro de Pesquisas 
                         Meteorol{\'o}gicas e Clim{\'a}ticas Aplicadas {\`a} Agricultura 
                         (CEPAGRI).} and {Universidade Estadual de Campinas (UNICAMP). 
                         Centro de Pesquisas Meteorol{\'o}gicas e Clim{\'a}ticas 
                         Aplicadas {\`a} Agricultura (CEPAGRI).} and {Embrapa 
                         Inform{\'a}tica Agropecu{\'a}ria (EMBRAPA). CNPTIA.}",
                title = "Estimativa de precipita{\c{c}}{\~a}o atrav{\'e}s de imagens do 
                         sat{\'e}lite GOES-8 utlizando redes neurais",
            booktitle = "Anais...",
                 year = "2003",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Fonseca, Leila Maria 
                         Garcia",
                pages = "1125--1127",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 11. (SBSR).",
            publisher = "INPE",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "neural network, goes-8, rainfall.",
             abstract = "Rainfall estimation based on available information from the five 
                         spectral channels of Goes 8 satellite is being developed using 
                         neural network technique as a subside for agricultural monitoring 
                         in the state of S. Paulo, Brazil. The test area is considered as 
                         the region explored by the Meteorological Radar of the 
                         Meteorological Research Institute at Bauru-SP, with a radius 
                         covering of 240 Km. For the month of February, 2001, samples have 
                         been selected with a minimum of three sequential images of the 
                         satellite, fifteen minutes interval of radar images and 20 minutes 
                         interval of precipitation data from surface automatic station. 
                         Data and images were transformed for the same temporal and spatial 
                         resolution. Data feeding for the neural network type Multi-layers 
                         Perceptrons (MLP) considers information about texture and cloud 
                         spectral characteristic, cloud type and system, and precipitation 
                         values observed by the radar and rain gages.",
  conference-location = "Belo Horizonte",
      conference-year = "5-10 abr. 2003",
                 isbn = "85-17-00017-X",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais",
                  ibi = "ltid.inpe.br/sbsr/2002/11.17.12.12",
                  url = "http://urlib.net/ibi/ltid.inpe.br/sbsr/2002/11.17.12.12",
           targetfile = "11_302.pdf",
                 type = "Sensoriamento Remoto da Atmosfera e Meteorologia / Meteorology and 
                         Atmospheric Remote Sensing",
        urlaccessdate = "03 maio 2024"
}