Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

Jiahui Tao, Yicheng Gu, Xin Yin, Junlai Chen, Tianqi Ao and Jianyun Zhang ()
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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
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

Sustainability, 2024, vol. 16, issue 19, 1-14

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.

Keywords: streamflow; prediction; SWAT; transformer; coupled modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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