@Article{MonegoAnocCamp:2022:SoAmSe,
                 issn = "2073-4433",
                title = "South America Seasonal Precipitation Prediction by 
                         Gradient-Boosting Machine-Learning Approach",
               number = "2",
           targetfile = "atmosphere-13-00243-v2.pdf",
                pages = "e243",
             keywords = "Gradient boosting, Machine learning, Precipitation, Seasonal 
                         climate prediction.",
               volume = "13",
             abstract = "Machine learning has experienced great success in many 
                         applications. Precipitation is a hard meteorological variable to 
                         predict, but it has a strong impact on society. Here, a 
                         machine-learning techniquea formulation of gradient-boosted 
                         treesis applied to climate seasonal precipitation prediction over 
                         South America. The Optuna framework, based on Bayesian 
                         optimization, was employed to determine the optimal 
                         hyperparameters for the gradient-boosting scheme. A comparison 
                         between seasonal precipitation forecasting among the numerical 
                         atmospheric models used by the National Institute for Space 
                         Research (INPE, Brazil) as an operational procedure for 
                         weather/climate forecasting, gradient boosting, and deep-learning 
                         techniques is made regarding observation, with some showing better 
                         performance for the boosting scheme.",
                  doi = "10.3390/atmos13020243",
                  url = "http://dx.doi.org/10.3390/atmos13020243",
               author = "Monego, Vinicius Schmidt and Anochi, Juliana Aparecida and Campos 
                         Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                month = "Feb.",
              journal = "Atmosphere",
             language = "en",
                 year = "2022",
        urlaccessdate = "2022, July 07"
}