@Article{LucioCoCaSeRaCa:2007:SoBr,
               author = "Lucio, P. S. and Conde, F. C. and Cavalcanti, Iracema Fonseca de 
                         Alabuquerque and Serrano, A. I. and Ramos, A. M. and Cardoso, A. 
                         O.",
          affiliation = "{Centro de Geof\ı} and sica de ´ Evora (CGE), Universidade 
                         de ´ Evora, Portugal/Departamento de Estat´\ı and stica 
                         (DEST, UFRN) and {Centro de Geof\ı} and sica de ´ Evora 
                         (CGE), Universidade de ´ Evora, Portugal/Departamento de 
                         Estat´\ı and stica (DEST, UFRN)",
                title = "Spatiotemporal monthly rainfall reconstruction via artificial 
                         neural network – case study: south of Brazil",
              journal = "Advances in Geosciences",
                 year = "2007",
               volume = "10",
                pages = "67--76",
                month = "Apr.",
             keywords = "rainfall, climatological, artificial neural network, atmospheric 
                         phenomena.",
             abstract = "Climatological records users, frequently, request time series for 
                         geographical locations where there is no observed meteorological 
                         attributes. Climatological conditions of the areas or points of 
                         interest have to be calculated interpolating observations in the 
                         time of neighboring stations and climate proxy. The aim of the 
                         present work is the application of reliable and robust procedures 
                         for monthly reconstruction of precipitation time series. Time 
                         series is a special case of symbolic regression and we can use 
                         Artificial Neural Network (ANN) to explore the spatiotemporal 
                         dependence of meteorological attributes. The ANN seems to be an 
                         important tool for the propagation of the related weather 
                         information to provide practical solution of uncertainties 
                         associated with interpolation, capturing the spatiotemporal 
                         structure of the data. In practice, one determines the embedding 
                         dimension of the time series attractor (delay time that determine 
                         how data are processed) and uses these numbers to define the 
                         networks architecture. Meteorological attributes can be accurately 
                         predicted by the ANN model architecture: designing, training, 
                         validation and testing; the best generalization of new data is 
                         obtained when the mapping represents the systematic aspects of the 
                         data, rather capturing the specific details of the particular 
                         training set. As illustration one takes monthly total rainfall 
                         series recorded in the period 1961 2005 in the Rio Grande do Sul 
                         Brazil. This reliable and robust reconstruction method has good 
                         performance and in particular, they were able to capture the 
                         intrinsic dynamic of atmospheric activities. The regional rainfall 
                         has been related to high-frequency atmospheric phenomena, such as 
                         El Nino and La Nina events, and low frequency phenomena, such as 
                         the Pacific Decadal Oscillation.",
           copyholder = "SID/SCD",
                 issn = "1680-7340 and 1680-7359",
             language = "en",
           targetfile = "Cavalcanti_adgeo-10-67-2007.pdf",
        urlaccessdate = "20 abr. 2024"
}