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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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Meteorologia
2024-01-01
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| Series: | Revista Brasileira de Meteorologia |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862023000100220&lng=en&tlng=en |
| _version_ | 1797361056352305152 |
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| author | Eluã Ramos Coutinho Jonni Guiller Ferreira Madeira Robson Mariano da Silva Elizabeth Mendes de Oliveira Angel Ramon Sanchez Delgado |
| author_facet | Eluã Ramos Coutinho Jonni Guiller Ferreira Madeira Robson Mariano da Silva Elizabeth Mendes de Oliveira Angel Ramon Sanchez Delgado |
| author_sort | Eluã Ramos Coutinho |
| collection | DOAJ |
| description | 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 humidity to six regions in the state of Rio de Janeiro (RJ), Brazil. The results were evaluated using statistical techniques and compared with results obtained by the Multiple Linear Regression (RLM), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models and also compared with the data recorded by the weather stations. The correlation coefficient (r) between the evaporation estimates generated by MLP-GA with the recorded data showed a high relationship, remaining between 0.82 to 0.97. The average percentage error (MPE) ranged from 6.01% to 9.67%, indicating a accuracy between 90% to 94%. For the maximum temperature generated by MLP-GA the correlation with the recorded data remained between 0.97 to 0.99. It also presented the MPE between 0.95% to 1.57%, maintaining the accuracy of the estimated data between 98% to 99%. The correlation coefficient (r) between the relative humidity estimates generated with the MLP-GA remained between 0.89 a 0.97, the MPE between 1.15% to 1.89%, which guaranteed a rate higher than 98% of correctness in its estimates. Such results demonstrated gains in relation to the other applied models and allowed the accomplishment of the filling of most of the missing values. |
| first_indexed | 2024-03-08T15:48:27Z |
| format | Article |
| id | doaj.art-1908f6adb1a440d5a33b81746ef36e4d |
| institution | Directory Open Access Journal |
| issn | 1982-4351 |
| language | English |
| last_indexed | 2024-03-08T15:48:27Z |
| publishDate | 2024-01-01 |
| publisher | Sociedade Brasileira de Meteorologia |
| record_format | Article |
| series | Revista Brasileira de Meteorologia |
| spelling | doaj.art-1908f6adb1a440d5a33b81746ef36e4d2024-01-09T07:44:08ZengSociedade Brasileira de MeteorologiaRevista Brasileira de Meteorologia1982-43512024-01-013810.1590/0102-778638220030Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time SeriesEluã Ramos Coutinhohttps://orcid.org/0000-0003-2350-4319Jonni Guiller Ferreira MadeiraRobson Mariano da SilvaElizabeth Mendes de OliveiraAngel Ramon Sanchez DelgadoAbstract 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 humidity to six regions in the state of Rio de Janeiro (RJ), Brazil. The results were evaluated using statistical techniques and compared with results obtained by the Multiple Linear Regression (RLM), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models and also compared with the data recorded by the weather stations. The correlation coefficient (r) between the evaporation estimates generated by MLP-GA with the recorded data showed a high relationship, remaining between 0.82 to 0.97. The average percentage error (MPE) ranged from 6.01% to 9.67%, indicating a accuracy between 90% to 94%. For the maximum temperature generated by MLP-GA the correlation with the recorded data remained between 0.97 to 0.99. It also presented the MPE between 0.95% to 1.57%, maintaining the accuracy of the estimated data between 98% to 99%. The correlation coefficient (r) between the relative humidity estimates generated with the MLP-GA remained between 0.89 a 0.97, the MPE between 1.15% to 1.89%, which guaranteed a rate higher than 98% of correctness in its estimates. Such results demonstrated gains in relation to the other applied models and allowed the accomplishment of the filling of most of the missing values.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862023000100220&lng=en&tlng=enfault fillingArtificial Neural NetworksGenetic Algorithms |
| spellingShingle | Eluã Ramos Coutinho Jonni Guiller Ferreira Madeira Robson Mariano da Silva Elizabeth Mendes de Oliveira Angel Ramon Sanchez Delgado Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series Revista Brasileira de Meteorologia fault filling Artificial Neural Networks Genetic Algorithms |
| title | Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series |
| title_full | Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series |
| title_fullStr | Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series |
| title_full_unstemmed | Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series |
| title_short | Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series |
| title_sort | application of a computational hybrid model to estimate and filling gaps for meteorological time series |
| topic | fault filling Artificial Neural Networks Genetic Algorithms |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862023000100220&lng=en&tlng=en |
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