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1. Identity statement
Reference TypeBook Section
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/4AGTE6P
Repositorysid.inpe.br/mtc-m21d/2024/01.09.13.56
Metadata Repositorysid.inpe.br/mtc-m21d/2024/01.09.13.56.38
Metadata Last Update2024:02.07.12.04.20 (UTC) administrator
Secondary KeyINPE--/
DOI10.1007/978-3-031-47036-3_19
ISBN978-303147035-6
Citation KeyMonegoAnocCamp:2024:UnQuCl
TitleUncertainty Quantification for Climate Precipitation Prediction by Decision Tree
Year2024
Access Date2024, May 16
Secondary TypePRE LI
2. Context
Author1 Monego, Vinicius Schmidt
2 Anochi, Juliana Aparecida
3 Campos Velho, Haroldo Fraga de
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHC3
Group1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
2 COPDT-CGIP-INPE-MCTI-GOV-BR
3 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 vinicius.monego@inpe.br
2 juliana.anochi@inpe.br
3 haroldo.camposvelho@inpe.br
EditorDe Cursi, J. E. Z.
Book TitleProceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling
PublisherSpringer
CityBerlin
Pages214-224
History (UTC)2024-01-09 13:56:38 :: simone -> administrator ::
2024-02-07 12:04:20 :: administrator -> simone :: 2024
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsClimate prediction
decision tree
precipitation
uncertainty quantification
AbstractNumerical 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.
AreaCOMP
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4. Conditions of access and use
Languageen
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Mirror Repositoryurlib.net/www/2021/06.04.03.40.25
Next Higher Units8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/46KUES5
DisseminationBNDEPOSITOLEGAL
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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