Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling
Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allo...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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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/S2666445324000242 |
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| author | Lavínia D. Balthazar Felix Miranda Vinícius B.R. Cândido Priscila Capriles Marconi Moraes CelsoB.M. Ribeiro Geane Fayer Leonardo Goliatt |
| author_facet | Lavínia D. Balthazar Felix Miranda Vinícius B.R. Cândido Priscila Capriles Marconi Moraes CelsoB.M. Ribeiro Geane Fayer Leonardo Goliatt |
| author_sort | Lavínia D. Balthazar |
| collection | DOAJ |
| description | Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allocation, flood management, and drought prediction scenarios. Machine learning (ML) techniques offer promising alternatives for improving long-term streamflow prediction. However, most existing studies on ML models for streamflow prediction have focused on shorter time horizons, limiting their broader applicability. Consequently, there is a need for dedicated research that addresses the use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the natural flow dynamics of a river, allowing for the simulation of reduced flow scenarios (25 % and 50 % reduction). This capability allows for simulating drought scenarios of varying severity, providing valuable insights for water service managers. This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts. |
| format | Article |
| id | doaj-art-0becbaf43a1e4e39a588d5bde32dba19 |
| 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-0becbaf43a1e4e39a588d5bde32dba192024-12-06T05:14:50ZengKeAi Communications Co., Ltd.Water Cycle2666-44532024-01-015266277Long-term natural streamflow forecasting under drought scenarios using data-intelligence modelingLavínia D. Balthazar0Felix Miranda1Vinícius B.R. Cândido2Priscila Capriles3Marconi Moraes4CelsoB.M. Ribeiro5Geane Fayer6Leonardo Goliatt7Federal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, BrazilArcelorMittal, Rodovia BR 040, Km 769, Barreira Do Triunfo, Juiz de Fora, 36092-901, Brazil; Rio de Janeiro State University, Rua São Francisco Xavier, 524, Maracanã, Rio de Janeiro, 20550-900, RJ, BrazilFederal University of Juiz de Fora, Rua José Lourenço Kelmer, S/N, Campus Universitário, Juiz de Fora, 36036-900, Brazil; Corresponding author.Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allocation, flood management, and drought prediction scenarios. Machine learning (ML) techniques offer promising alternatives for improving long-term streamflow prediction. However, most existing studies on ML models for streamflow prediction have focused on shorter time horizons, limiting their broader applicability. Consequently, there is a need for dedicated research that addresses the use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the natural flow dynamics of a river, allowing for the simulation of reduced flow scenarios (25 % and 50 % reduction). This capability allows for simulating drought scenarios of varying severity, providing valuable insights for water service managers. This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts.http://www.sciencedirect.com/science/article/pii/S2666445324000242Streamflow forecastingMachine learning pipelinesPiracicaba river |
| spellingShingle | Lavínia D. Balthazar Felix Miranda Vinícius B.R. Cândido Priscila Capriles Marconi Moraes CelsoB.M. Ribeiro Geane Fayer Leonardo Goliatt Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling Water Cycle Streamflow forecasting Machine learning pipelines Piracicaba river |
| title | Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling |
| title_full | Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling |
| title_fullStr | Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling |
| title_full_unstemmed | Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling |
| title_short | Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling |
| title_sort | long term natural streamflow forecasting under drought scenarios using data intelligence modeling |
| topic | Streamflow forecasting Machine learning pipelines Piracicaba river |
| url | http://www.sciencedirect.com/science/article/pii/S2666445324000242 |
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