@InProceedings{SilvaMellFons:2015:InArCo,
               author = "Silva, Alexsandro C{\^a}ndido de Oliveira and Mello, Marcio Pupin 
                         and Fonseca, Leila Maria Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Intelig{\^e}ncia Artificial como ferramenta para direcionar a 
                         expans{\~a}o sustent{\'a}vel da cana-de-a{\c{c}}{\'u}car no 
                         Estado de S{\~a}o Paulo",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3423--3430",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Predictive models have been used to understand several phenomena 
                         in the field of Earth sciences. Bayesian networks have become 
                         increasingly popular due to its potential to model such phenomena 
                         and, through graphical representation, state the relationship 
                         among variables with probabilistic models associated. This paper 
                         presents an improved algorithm of Bayesian networks, implemented 
                         in R software, able of handling raster data for remote sensing 
                         applications: e-BayNeRD (enhanced Bayesian Network for Raster 
                         Data). A case study was used to describe the main changes and test 
                         the enhanced version of the algorithm. Based on observed values 
                         for terrain slope, soil and fertility, edaphoclimatic aptitude and 
                         the Agri-environmental zoning, suitable areas for sustainable 
                         expansion of sugarcane in S{\~a}o Paulo State were mapped.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "677",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4BM2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4BM2",
           targetfile = "p0677.pdf",
                 type = "Geoprocessamento e aplica{\c{c}}{\~o}es",
        urlaccessdate = "27 abr. 2024"
}