MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region

Gomes, Evanice Pinheiro; Holanda, Patrícia da Silva; Júnior, José Francisco de Oliveira

doi:10.1007/s10661-022-09939-0

Environ Monit Assess

2022

DOI: 10.1007/s10661-022-09939-0

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MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region

Evanice Pinheiro Gomes

Patrícia da Silva Holanda

José Francisco de Oliveira Júnior

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Cited by 2 publications

References 54 publications

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Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Pinheiro Gomes,

Progênio,

da Silva Holanda

2024

Clim Dyn

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Hydrological analyzes carried out from precipitation in the Legal Amazon (AMZ-L) are essential due to their importance in climate regulation, regional and global atmospheric circulation. However, in this region, there are limitations related to data series with short periods and many gaps, especially in the daily scale. Thus, to improve precipitation analyses, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in AMZ-L. For this, 22 rainfall stations were adopted over a period of 18 years (1998-2016) and with <1% missing data, which were organized considering the complete series and the seasonal periods (rainy and dry). The results obtained demonstrate the good capacity of the model to preserve the precipitation characteristics of the evaluated rainfall stations, mainly those with a more humid climate and with more frequent precipitations during the year, as is the case of those located in the Amazon Biome. However, in regions that suffer prolonged periods of drought, 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 to the model, except for some rainfall gauge stations located at latitudes close to the equator.However, this study may support future research on the estimation of daily precipitation in the region.

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Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Pinheiro Gomes,

Progênio,

da Silva Holanda

2024

Clim Dyn

Self Cite

View full text Add to dashboard Cite

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Approaches for the short-term prediction of natural daily streamflows using hybrid machine learning enhanced with grey wolf optimization

Martinho

Saporetti

Goliatt

2022

Hydrological Sciences Journal

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MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region

Cited by 2 publications

References 54 publications

Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Approaches for the short-term prediction of natural daily streamflows using hybrid machine learning enhanced with grey wolf optimization

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