Hybrid model for daily streamflow and phosphorus load prediction

Abstract

Environmental factors, such as climate change and land use changes, affect water quality drastically. To consider these, various predictive models, both process-based and data-driven, have been used. However, each model has distinct limitations. In this study, a hybrid model combining the soil and water assessment tool and the reverse time attention mechanism (SWAT–RETAIN) was proposed for predicting daily streamflow and total phosphorus (TP) load of a watershed. SWAT–RETAIN was applied to Hwangryong River, South Korea. The hybrid model uses the SWAT output as input data for the RETAIN. Spatial, meteorological, and hydrological data were collected to develop the SWAT to generate high temporal resolution data. RETAIN facilitated effective simultaneous prediction. The SWAT–RETAIN exhibited high accuracy in predicting streamflow (Nash–Sutcliffe efficiency (NSE): 0.45, root mean square error (RMSE): 27.74, percent bias (PBIAS): 22.63 for test sets), and TP load (NSE: 0.50, RMSE: 423.93, PBIAS: 22.09 for test sets). This result was evident in the performance evaluation using flow duration and load duration curves. The SWAT–RETAIN provides enhanced temporal resolution and performance, enabling the simultaneous prediction of multiple variables. It can be applied to predict various water quality variables in larger watersheds. HIGHLIGHTS Process-based and data-driven models combined for improved prediction accuracy.; Output of SWAT was used as input data for RETAIN.; SWAT–RETAIN yielded superior predictive power over standalone SWAT.; Trends in observed values were accurately captured.; Applicable to time-series prediction of other water quality variables.;

Keywords

• deep learning
• hybrid model
• phosphorus load
• reverse time attention mechanism
• soil and water assessment tool
• streamflow