@InCollection{MonegoAnocCamp:2024:UnQuCl,
               author = "Monego, Vinicius Schmidt and Anochi, Juliana Aparecida and Campos 
                         Velho, Haroldo Fraga de",
                title = "Uncertainty Quantification for Climate Precipitation Prediction by 
                         Decision Tree",
            booktitle = "Proceedings of the 6th International Symposium on Uncertainty 
                         Quantification and Stochastic Modelling",
            publisher = "Springer",
                 year = "2024",
               editor = "De Cursi, J. E. Z.",
                pages = "214--224",
              address = "Berlin",
             keywords = "Climate prediction, decision tree, precipitation, uncertainty 
                         quantification.",
             abstract = "Numerical weather and climate prediction have been addressed by 
                         numerical methods. This approach has been under permanent 
                         development. In order to estimate the degree of confidence on a 
                         prediction, an ensemble prediction has been adopted. Recently, 
                         machine learning algorithms have been employed for many 
                         applications. Here, the con- fidence interval for the 
                         precipitation climate prediction is addressed by a decision tree 
                         algorithm, by using the Light Gradient Boosting Machine (LightGBM) 
                         framework. The best hyperparameters for the LightGBM models were 
                         determined by the Optuna hyperparameter optimization framework, 
                         which uses a Bayesian approach to calculate an optimal 
                         hyperparameter set. Numerical experiments were carried out over 
                         South America. LightGBM is a supervised machine-learning 
                         technique. A period from January-1980 up to December-2017 was em- 
                         ployed for the learning phase, and the years 2018 and 2019 were 
                         used for testing, showing very good results.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-3-031-47036-3_19",
                  url = "http://dx.doi.org/10.1007/978-3-031-47036-3_19",
                 isbn = "978-303147035-6",
             language = "en",
        urlaccessdate = "12 maio 2024"
}