Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon.
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| Title: | Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon. |
|---|---|
| Authors: | Pinheiro Gomes, Evanice1 (AUTHOR) gomesevanice@ufpa.br, Progênio, Mayke Feitosa2 (AUTHOR), da Silva Holanda, Patrícia3 (AUTHOR) |
| Source: | Climate Dynamics. Jul2024, Vol. 62 Issue 7, p6219-6233. 15p. |
| Subjects: | Stochastic learning models, Machine learning, Artificial neural networks, Atmospheric circulation, Water supply, Rain gauges |
| Abstract: | Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, data series with short periods and many gaps, especially at the daily scale, are a limitation in this region. In order to improve precipitation analysis, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in the BLA. For this purpose, 22 rain gauge stations were adopted and organized, taking into account the complete series and the seasonal periods (rainy and dry).The results obtained showed a good performance of the model, with ranges of MSE (0.0022–0.2665), MAPE (0.0083–1.5343) and RMSE (0.0017–0.0214), which characterize an acceptable estimate for the estimation daily precipitation, especially in those with a wetter climate and more frequent precipitation during the year, as is the case in those located in the Amazon Biome. However, in regions that suffer from droughts, such as the Amazon-Cerrado ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates for the model, except for some rain gauge stations located at latitudes close to the equator. However, this study could support future research on the estimation of daily precipitation in the region. [ABSTRACT FROM AUTHOR] |
| Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179815403 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pinheiro+Gomes%2C+Evanice%22">Pinheiro Gomes, Evanice</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gomesevanice@ufpa.br</i><br /><searchLink fieldCode="AR" term="%22Progênio%2C+Mayke+Feitosa%22">Progênio, Mayke Feitosa</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22da+Silva+Holanda%2C+Patrícia%22">da Silva Holanda, Patrícia</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Climate+Dynamics%22">Climate Dynamics</searchLink>. Jul2024, Vol. 62 Issue 7, p6219-6233. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stochastic+learning+models%22">Stochastic learning models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+circulation%22">Atmospheric circulation</searchLink><br /><searchLink fieldCode="DE" term="%22Water+supply%22">Water supply</searchLink><br /><searchLink fieldCode="DE" term="%22Rain+gauges%22">Rain gauges</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, data series with short periods and many gaps, especially at the daily scale, are a limitation in this region. In order to improve precipitation analysis, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in the BLA. For this purpose, 22 rain gauge stations were adopted and organized, taking into account the complete series and the seasonal periods (rainy and dry).The results obtained showed a good performance of the model, with ranges of MSE (0.0022–0.2665), MAPE (0.0083–1.5343) and RMSE (0.0017–0.0214), which characterize an acceptable estimate for the estimation daily precipitation, especially in those with a wetter climate and more frequent precipitation during the year, as is the case in those located in the Amazon Biome. However, in regions that suffer from droughts, such as the Amazon-Cerrado ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates for the model, except for some rain gauge stations located at latitudes close to the equator. However, this study could support future research on the estimation of daily precipitation in the region. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00382-024-07200-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 6219 Subjects: – SubjectFull: Stochastic learning models Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Atmospheric circulation Type: general – SubjectFull: Water supply Type: general – SubjectFull: Rain gauges Type: general Titles: – TitleFull: Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pinheiro Gomes, Evanice – PersonEntity: Name: NameFull: Progênio, Mayke Feitosa – PersonEntity: Name: NameFull: da Silva Holanda, Patrícia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09307575 Numbering: – Type: volume Value: 62 – Type: issue Value: 7 Titles: – TitleFull: Climate Dynamics Type: main |
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