IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i19p8699-d1494629.html
   My bibliography  Save this article

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

Author

Listed:
  • Jiahui Tao

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, China
    Institute of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, No. 223, Guangzhou Road, Nanjing 210029, China)

  • Yicheng Gu

    (Institute of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, No. 223, Guangzhou Road, Nanjing 210029, China)

  • Xin Yin

    (Institute of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, No. 223, Guangzhou Road, Nanjing 210029, China)

  • Junlai Chen

    (College of Water Resources and Architectural Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling 712100, China)

  • Tianqi Ao

    (Institute of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, No. 223, Guangzhou Road, Nanjing 210029, China)

  • Jianyun Zhang

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, China
    Institute of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, No. 223, Guangzhou Road, Nanjing 210029, China)

Abstract

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Suggested Citation

  • Jiahui Tao & Yicheng Gu & Xin Yin & Junlai Chen & Tianqi Ao & Jianyun Zhang, 2024. "Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction," Sustainability, MDPI, vol. 16(19), pages 1-14, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8699-:d:1494629
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/19/8699/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/19/8699/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Dejian & Chen, Xingwei & Yao, Huaxia & Lin, Bingqing, 2015. "Improved calibration scheme of SWAT by separating wet and dry seasons," Ecological Modelling, Elsevier, vol. 301(C), pages 54-61.
    2. Huiqi Deng & Wenjie Chen & Guoru Huang, 2022. "Deep insight into daily runoff forecasting based on a CNN-LSTM model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1675-1696, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    2. Lijun Jiao & Ruimin Liu & Linfang Wang & Lin Li & Leiping Cao, 2021. "Evaluating Spatiotemporal Variations in the Impact of Inter-basin Water Transfer Projects in Water-receiving Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5409-5429, December.
    3. Kotapati Narayana Loukika & Venkata Reddy Keesara & Eswar Sai Buri & Venkataramana Sridhar, 2022. "Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    4. Molina-Navarro, Eugenio & Hallack-Alegría, Michelle & Martínez-Pérez, Silvia & Ramírez-Hernández, Jorge & Mungaray-Moctezuma, Alejandro & Sastre-Merlín, Antonio, 2016. "Hydrological modeling and climate change impacts in an agricultural semiarid region. Case study: Guadalupe River basin, Mexico," Agricultural Water Management, Elsevier, vol. 175(C), pages 29-42.
    5. De Girolamo, Anna Maria & Barca, Emanuele & Pappagallo, Giuseppe & Lo Porto, Antonio, 2017. "Simulating ecologically relevant hydrological indicators in a temporary river system," Agricultural Water Management, Elsevier, vol. 180(PB), pages 194-204.
    6. Tingqi Wang & Yuting Guo & Mazina Svetlana Evgenievna & Zhenjiang Wu, 2024. "Application of a Multi-Model Fusion Forecasting Approach in Runoff Prediction: A Case Study of the Yangtze River Source Region," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    7. Dan Li & Bingjun Liu & Changqing Ye, 2022. "Meteorological and hydrological drought risks under changing environment on the Wanquan River Basin, Southern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2941-2967, December.
    8. Rumph Frederiksen, Rasmus & Molina-Navarro, Eugenio, 2021. "The importance of subsurface drainage on model performance and water balance in an agricultural catchment using SWAT and SWAT-MODFLOW," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Bisrat Ayalew Yifru & Il-Moon Chung & Min-Gyu Kim & Sun Woo Chang, 2020. "Assessment of Groundwater Recharge in Agro-Urban Watersheds Using Integrated SWAT-MODFLOW Model," Sustainability, MDPI, vol. 12(16), pages 1-18, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8699-:d:1494629. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.