Bridging process-based hydrology and deep learning: exploring hybrid MIKE NAM–LSTM techniques for accurate streamflow prediction in a humid tropical river basin in India.
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| Title: | Bridging process-based hydrology and deep learning: exploring hybrid MIKE NAM–LSTM techniques for accurate streamflow prediction in a humid tropical river basin in India. |
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| Authors: | Chandran, Divya1 (AUTHOR) divyac86@gmail.com, Wardah, Haleema1 (AUTHOR), Chithra, N R1 (AUTHOR) |
| Source: | Journal of Earth System Science. Jun2026, Vol. 135 Issue 2, p1-15. 15p. |
| Subject Terms: | *Streamflow, *Long short-term memory, *Water management, *Hydrologic models, *Hydrological forecasting, *Watersheds |
| Geographic Terms: | India |
| Abstract: | Accurate streamflow prediction is crucial for disaster prevention, mitigation, and water resource management. While data-driven approaches surpass the traditional process-based models, they are often questioned for their physical interpretability. Hybrid modelling frameworks that integrate hydrological process knowledge with data-driven techniques have emerged as a promising strategy to enhance streamflow prediction accuracy while maintaining physical interpretability. This study explores the potential of hybrid models that integrate the physically based MIKE NAM model with advanced machine learning techniques such as Long Short Term Memory (LSTM). Two hybrid configurations were developed: one using MIKE NAM-simulated streamflow as input to the LSTM, and another using both MIKE NAM-simulated streamflow and individual NAM components (overland flow, baseflow, interflow, and evapotranspiration) to enhance residual learning. The models are applied to predict 1-day streamflow in the Meenachil River Basin, India, and the performance of each model is evaluated using MAE, RMSE, R2, NSE, and KGE metrics across full-period and disaggregated flow regimes such as low, medium, and high flows. Results indicate that hybrid models significantly outperform both standalone MIKE NAM and LSTM models, even in diverse hydrological conditions (Kling Gupta Efficiency, KGEMIKE = 0.62, KGELSTM = 0.75, KGEHYBRID_I = 0.94, KGEHYBRID_2 = 0.93). This work demonstrates the potential of hybrid frameworks in bridging the gap between physically based understanding and data-driven accuracy, providing a reliable solution for operational forecasting and water resource planning in data-scarce and hydrologically complex environments. Research highlights: Hybrid NAM-LSTM models enhance streamflow prediction (KGE=0.93), outperforming standalone MIKE NAM (KGE = 0.62) and LSTM model (KGE = 0.75). Integration of NAM components enhances residual learning, improving sensitivity to hydrological processes. Hybrid models demonstrate strong potential for operational forecasting applications as demonstrated by enhanced extreme predictions. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193197834 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bridging process-based hydrology and deep learning: exploring hybrid MIKE NAM–LSTM techniques for accurate streamflow prediction in a humid tropical river basin in India. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chandran%2C+Divya%22">Chandran, Divya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> divyac86@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wardah%2C+Haleema%22">Wardah, Haleema</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chithra%2C+N+R%22">Chithra, N R</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Earth+System+Science%22">Journal of Earth System Science</searchLink>. Jun2026, Vol. 135 Issue 2, p1-15. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrological+forecasting%22">Hydrological forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate streamflow prediction is crucial for disaster prevention, mitigation, and water resource management. While data-driven approaches surpass the traditional process-based models, they are often questioned for their physical interpretability. Hybrid modelling frameworks that integrate hydrological process knowledge with data-driven techniques have emerged as a promising strategy to enhance streamflow prediction accuracy while maintaining physical interpretability. This study explores the potential of hybrid models that integrate the physically based MIKE NAM model with advanced machine learning techniques such as Long Short Term Memory (LSTM). Two hybrid configurations were developed: one using MIKE NAM-simulated streamflow as input to the LSTM, and another using both MIKE NAM-simulated streamflow and individual NAM components (overland flow, baseflow, interflow, and evapotranspiration) to enhance residual learning. The models are applied to predict 1-day streamflow in the Meenachil River Basin, India, and the performance of each model is evaluated using MAE, RMSE, R2, NSE, and KGE metrics across full-period and disaggregated flow regimes such as low, medium, and high flows. Results indicate that hybrid models significantly outperform both standalone MIKE NAM and LSTM models, even in diverse hydrological conditions (Kling Gupta Efficiency, KGEMIKE = 0.62, KGELSTM = 0.75, KGEHYBRID_I = 0.94, KGEHYBRID_2 = 0.93). This work demonstrates the potential of hybrid frameworks in bridging the gap between physically based understanding and data-driven accuracy, providing a reliable solution for operational forecasting and water resource planning in data-scarce and hydrologically complex environments. Research highlights: Hybrid NAM-LSTM models enhance streamflow prediction (KGE=0.93), outperforming standalone MIKE NAM (KGE = 0.62) and LSTM model (KGE = 0.75). Integration of NAM components enhances residual learning, improving sensitivity to hydrological processes. Hybrid models demonstrate strong potential for operational forecasting applications as demonstrated by enhanced extreme predictions. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193197834 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12040-026-02798-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Streamflow Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Water management Type: general – SubjectFull: Hydrologic models Type: general – SubjectFull: Hydrological forecasting Type: general – SubjectFull: Watersheds Type: general – SubjectFull: India Type: general Titles: – TitleFull: Bridging process-based hydrology and deep learning: exploring hybrid MIKE NAM–LSTM techniques for accurate streamflow prediction in a humid tropical river basin in India. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chandran, Divya – PersonEntity: Name: NameFull: Wardah, Haleema – PersonEntity: Name: NameFull: Chithra, N R IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02534126 Numbering: – Type: volume Value: 135 – Type: issue Value: 2 Titles: – TitleFull: Journal of Earth System Science Type: main |
| ResultId | 1 |
