@Article{ValverdeRamirezCampFerr:2005:ArNeNe,
               author = "Valverde Ramirez, Maria Cleofe and Campos Velho, Haroldo Fraga de 
                         and Ferreira, Nelson Jesus",
          affiliation = "{CPTEC-INPE-Cachoeira Paulista-12630-000-SP-Brasil}",
                title = "Artificial neural network technique for rainfall forecasting 
                         applied to the Sao Paulo region",
              journal = "Journal of Hydrology",
                 year = "2005",
               volume = "301",
               number = "1-4",
                pages = "146--162",
                month = "Jan.",
             keywords = "artificial neural network, statistical rainfall forecast, multiple 
                         linear regression, Regional ETA model.",
             abstract = "An artificial neural network (ANN) technique is used to construct 
                         a nonlinear mapping between output data from a regional ETA model 
                         ran at the Center for Weather Forecasts and Climate 
                         Studies/National Institute for Space Research/Brazil, and surface 
                         rainfall data for the region of S{\~a}o Paulo State, Brazil. The 
                         objective is to generate site-specific quantitative forecasts of 
                         daily rainfall. The test was performed for six locations in 
                         S{\~a}o Paulo State during the austral summer and winter of the 
                         1997/2002 period. The analysis was made using a feedforward neural 
                         network and resilient propagation learning algorithm. 
                         Meteorological variables from the ETA model (potential 
                         temperature, vertical component of the wind, specific humidity, 
                         air temperature, precipitable water, relative vorticity and 
                         moisture divergence flux) are used as input data to the trained 
                         networks, which generate rainfall forecast for the next time step. 
                         Additionally, predictions with a multiple linear regression model 
                         were compared to those of ANN. In order to evaluate the rainfall 
                         forecast skill over the studied region a statistical analysis was 
                         performed. The results show that ANN forecasts were superior to 
                         the ones obtained by the linear regression model thus revealing a 
                         great potential for an operational suite.",
           copyholder = "SID/SCD",
                 issn = "0022-1694",
             language = "en",
           targetfile = "Ramirez_Artificial neural network.pdf.pdf",
        urlaccessdate = "21 maio 2024"
}