Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach
Accurate short-term streamflow forecasting models are crucial for effective water resource management, enabling timely responses to extreme flood or drought events and mitigating potential socioeconomic damage. This study proposes robust hybrid Wavelet Artificial Neural Network (WANN) models for rea...
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Water Cycle |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266644532400028X |
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| author | Yulia Gorodetskaya Rodrigo Oliveira Silva Celso Bandeira de Melo Ribeiro Leonardo Goliatt |
| author_facet | Yulia Gorodetskaya Rodrigo Oliveira Silva Celso Bandeira de Melo Ribeiro Leonardo Goliatt |
| author_sort | Yulia Gorodetskaya |
| collection | DOAJ |
| description | Accurate short-term streamflow forecasting models are crucial for effective water resource management, enabling timely responses to extreme flood or drought events and mitigating potential socioeconomic damage. This study proposes robust hybrid Wavelet Artificial Neural Network (WANN) models for real-world hydrological applications. Two WANN variants, WANNone and WANNmulti, are proposed for short-term streamflow forecasting of extreme (high and low) flows at eight gauging stations within Brazil's Paraíba do Sul River basin. WANNone directly feeds both the original streamflow data and the decomposed components obtained through an À Trous wavelet transform into the ANN architecture. Conversely, WANNmulti utilizes separate ANNs for the original data, with the final streamflow estimate reconstructed via the inverse wavelet transform of the individual ANN outputs. The performance of these WANN models is then compared against conventional ANN models. In both approaches, Bayesian optimization is employed to fine-tune the hyperparameters within the ANN architecture. The WANN models achieved superior performance for 7-day streamflow forecasts compared to conventional ANN models. WANN models yielded high R2 values (>0.9) and low MAPE (4.8%–14.7%) within the expected RMSE range, demonstrating statistically significant improvements over ANN models (71% and 75% reduction in RMSE and MAPE, respectively, and 69% increase in R2). Further analysis revealed that WANNmulti models generally exhibited superior performance for low extreme flow predictions, while WANNone models achieved the highest accuracy for high extreme flows at most stations. WANN models' strong performance suggests their value for real-time flood warnings, enabling improved decision-making in areas like flood/drought mitigation and urban water planning. |
| format | Article |
| id | doaj-art-8880d57e1a5a48a19c423c0ce7fc3fba |
| institution | Kabale University |
| issn | 2666-4453 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Water Cycle |
| spelling | doaj-art-8880d57e1a5a48a19c423c0ce7fc3fba2024-12-06T05:14:51ZengKeAi Communications Co., Ltd.Water Cycle2666-44532024-01-015297312Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approachYulia Gorodetskaya0Rodrigo Oliveira Silva1Celso Bandeira de Melo Ribeiro2Leonardo Goliatt3Department of Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, 36036-900, BrazilDepartment of Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, 36036-900, BrazilDepartment of Sanitary and Environmental Engineering, Federal University of Juiz de Fora, Juiz de Fora, 36036-900, BrazilDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, 36036-900, Brazil; Corresponding author.Accurate short-term streamflow forecasting models are crucial for effective water resource management, enabling timely responses to extreme flood or drought events and mitigating potential socioeconomic damage. This study proposes robust hybrid Wavelet Artificial Neural Network (WANN) models for real-world hydrological applications. Two WANN variants, WANNone and WANNmulti, are proposed for short-term streamflow forecasting of extreme (high and low) flows at eight gauging stations within Brazil's Paraíba do Sul River basin. WANNone directly feeds both the original streamflow data and the decomposed components obtained through an À Trous wavelet transform into the ANN architecture. Conversely, WANNmulti utilizes separate ANNs for the original data, with the final streamflow estimate reconstructed via the inverse wavelet transform of the individual ANN outputs. The performance of these WANN models is then compared against conventional ANN models. In both approaches, Bayesian optimization is employed to fine-tune the hyperparameters within the ANN architecture. The WANN models achieved superior performance for 7-day streamflow forecasts compared to conventional ANN models. WANN models yielded high R2 values (>0.9) and low MAPE (4.8%–14.7%) within the expected RMSE range, demonstrating statistically significant improvements over ANN models (71% and 75% reduction in RMSE and MAPE, respectively, and 69% increase in R2). Further analysis revealed that WANNmulti models generally exhibited superior performance for low extreme flow predictions, while WANNone models achieved the highest accuracy for high extreme flows at most stations. WANN models' strong performance suggests their value for real-time flood warnings, enabling improved decision-making in areas like flood/drought mitigation and urban water planning.http://www.sciencedirect.com/science/article/pii/S266644532400028XArtificial neural networksWavelet transformTime seriesStreamflow forecastingParaíba do Sul riverExtreme flows |
| spellingShingle | Yulia Gorodetskaya Rodrigo Oliveira Silva Celso Bandeira de Melo Ribeiro Leonardo Goliatt Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach Water Cycle Artificial neural networks Wavelet transform Time series Streamflow forecasting Paraíba do Sul river Extreme flows |
| title | Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach |
| title_full | Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach |
| title_fullStr | Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach |
| title_full_unstemmed | Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach |
| title_short | Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach |
| title_sort | enhancing short term streamflow forecasting of extreme events a wavelet artificial neural network hybrid approach |
| topic | Artificial neural networks Wavelet transform Time series Streamflow forecasting Paraíba do Sul river Extreme flows |
| url | http://www.sciencedirect.com/science/article/pii/S266644532400028X |
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