@InCollection{MonegoAnocCamp:2023: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, Cham",
year = "2023",
editor = "Cursi, J. E. S.",
pages = "214--224",
keywords = "quantification, climate precipitation, decision tree.",
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 = "{} 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 = "9783031470",
label = "lattes: 2720072834057575 2 MonegoAnocCamp:2023:LeNoMe",
language = "en",
urlaccessdate = "16 jun. 2024"
}