Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir
This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-09-01
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| Series: | Proceedings of the International Association of Hydrological Sciences |
| Online Access: | https://www.proc-iahs.net/364/100/2014/piahs-364-100-2014.pdf |
| _version_ | 1819298690014117888 |
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| author | C. A. G. Santos P. K. M. M. Freire G. B. L. Silva R. M. Silva |
| author_facet | C. A. G. Santos P. K. M. M. Freire G. B. L. Silva R. M. Silva |
| author_sort | C. A. G. Santos |
| collection | DOAJ |
| description | This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error <i>RMSE</i> decreased by more than 50% (i.e. from 454.2828 to 200.0483). |
| first_indexed | 2024-12-24T05:33:54Z |
| format | Article |
| id | doaj.art-3a16c151d46a4dc986042b03f2287b0f |
| institution | Directory Open Access Journal |
| issn | 2199-8981 2199-899X |
| language | English |
| last_indexed | 2024-12-24T05:33:54Z |
| publishDate | 2014-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Proceedings of the International Association of Hydrological Sciences |
| spelling | doaj.art-3a16c151d46a4dc986042b03f2287b0f2022-12-21T17:13:05ZengCopernicus PublicationsProceedings of the International Association of Hydrological Sciences2199-89812199-899X2014-09-0136410010510.5194/piahs-364-100-2014Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoirC. A. G. Santos0P. K. M. M. Freire1G. B. L. Silva2R. M. Silva3Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 João Pessoa – PB, BrazilDepartment of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 João Pessoa – PB, BrazilDepartment of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 João Pessoa – PB, BrazilDepartment of Geosciences, Federal University of Paraíba, 58051-900 João Pessoa – PB, BrazilThis paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error <i>RMSE</i> decreased by more than 50% (i.e. from 454.2828 to 200.0483).https://www.proc-iahs.net/364/100/2014/piahs-364-100-2014.pdf |
| spellingShingle | C. A. G. Santos P. K. M. M. Freire G. B. L. Silva R. M. Silva Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir Proceedings of the International Association of Hydrological Sciences |
| title | Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir |
| title_full | Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir |
| title_fullStr | Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir |
| title_full_unstemmed | Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir |
| title_short | Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir |
| title_sort | discrete wavelet transform coupled with ann for daily discharge forecasting into tres marias reservoir |
| url | https://www.proc-iahs.net/364/100/2014/piahs-364-100-2014.pdf |
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