Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series

Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series

Abstract The present study applies computational intelligence techniques in the development of a hybrid model composed of Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) (MLP-GA) to estimate and fill in the gaps in the monthly variables of evaporation, maximum temperature and relative...

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Bibliographic Details
Main Authors: Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira, Robson Mariano da Silva, Elizabeth Mendes de Oliveira, Angel Ramon Sanchez Delgado
Format: Article
Language:English
Published: Sociedade Brasileira de Meteorologia 2024-01-01
Series:Revista Brasileira de Meteorologia
Subjects:
fault filling
Artificial Neural Networks
Genetic Algorithms
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862023000100220&lng=en&tlng=en

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http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862023000100220&lng=en&tlng=en

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