A GRADIENT BOOSTING MODEL OPTIMIZED BY A GENETIC ALGORITHM FOR SHORT-TERM RIVERFLOW FORECAST

Tales Lima Fonseca, Yulia Gorodetskaya, Gisele Goulart Tavares, Celso Bandeira de Melo Ribeiro, Leonardo Goliatt da Fonseca

Resumo


The short-term streamflow forecast is an important parameter in studies related to energy generation and the prediction of possible floods. Flowing through three Brazilian states, the Paraíba do Sul river is responsible for the supply and energy generation in several municipalities.  Machine learning techniques have been studied with the aim of improving these predictions through the use of hydrological and hydrometeorological parameters. Furthermore, the predictive performance of the machine learning techniques are directly related to the quality of the training base and, moreover, to the set of hyperparameters used. The present study explores the combination of the Gradient Boosting technique coupled with a Genetic Algorithm to found the best set of hyperparameter to maximize the predicting performance of the Paraíba do Sul river streamflow.


Palavras-chave


Streamflow prediction; Gradient Boosting; Paraíba do Sul; Genetic Algorithm

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DOI: http://dx.doi.org/10.21575/25254782rmetg2019vol4n3845

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Direitos autorais 2019 Tales Lima Fonseca, Yulia Gorodetskaya, Gisele Goulart Tavares, Celso Bandeira de Melo Ribeiro, Leonardo Goliatt da Fonseca

Revista Mundi Engenharia, Tecnologia e Gestão ISSN 2525-4782

Qualis: B4 - Interdisciplinar, B5 - Geografia, B5 - Administração Pública e de Empresas, Ciências Contábeis e Turismo, B5 - Comunicação e Informação, B5 - Engenharias III

 

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