Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin

Ziyi Mei, Tao Peng (), Lu Chen (), Vijay P. Singh, Bin Yi, Zhiyuan Leng, Xiaoxue Gan and Tao Xie
Additional contact information
Ziyi Mei: Huazhong University of Science and Technology
Tao Peng: China Three Gorges University
Lu Chen: Huazhong University of Science and Technology
Vijay P. Singh: Texas A&M University
Bin Yi: Huazhong University of Science and Technology
Zhiyuan Leng: Huazhong University of Science and Technology
Xiaoxue Gan: Huazhong University of Science and Technology
Tao Xie: Huazhong University of Science and Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 1, No 18, 397-418

Abstract: Abstract Simulation of watershed streamflow is essential for the prevention and control of flood and drought disasters. To improve streamflow simulation, a coupled SWAT-LSTM model was constructed by combining a conceptual process-based hydrological model—Soil and Water Assessment Tool (SWAT)—with a machine learning model—Long Short-Term Memory (LSTM). The coupled model was applied to simulate the daily streamflow of the upper Huaihe River above the Xixian station from 1962 to 2010, which identified the optimal explanatory variables of the model and reduced streamflow simulation errors. Furthermore, four machine learning models, back propagation (BP) neural network, gated recurrent unit (GRU), support vector regression (SVR) and extreme gradient boosting (XGBoost), were chosen to assess the effectiveness of coupling SWAT with LSTM in streamflow simulation. Results showed that the coupled SWAT-LSTM model performed satisfactorily in streamflow simulation in the study area, with NSE reaching 0.90 and 0.85 in calibration and validation periods, respectively. The coupled model showed a significant improvement in simulating flood peak and average streamflow in each period, with mean NSE increasing by 0.24 compared to the standalone SWAT model. In comparison to other coupled models (i.e., SWAT-BP, SWAT-GRU, SWAT-SVR, and SWAT-XGB), the mean NSE of SWAT-LSTM exhibited an improvement of 0.02–0.16 during validation period. Furthermore, the coupled model effectively avoided the overfitting problem and had better generalization performance. The findings of this study offer new ideas for streamflow simulation of watersheds and provide references for water resources management and planning.

Keywords: Machine learning; SWAT; LSTM; Coupled modeling; Streamflow simulation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03975-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:1:d:10.1007_s11269-024-03975-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03975-w

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().