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Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
Study region: Chao Phraya River Basin—a major river with unique characteristics located in Thailand. Study focus: This study sought to simulate the flow rates in the Chao Phraya River Basin, which is a tidal river that poses challenges to traditional modeling approaches. The soil and water assessment tool (SWAT) is a hydrological model extensively employed for simulating flow rates. However, limitations arise in applying the SWAT model to the Chao Phraya River Basin due to its tidal nature, resulting in an unsatisfactory model performance. To address this, a long short-term memory (LSTM) model, i.e., the SWAT–LSTM model, was introduced to complement the SWAT model. New hydrological insights for the Region: The collaborative coupling of hydrological information derived from the SWAT and LSTM notably enhanced the model performance. This improvement was assessed using the Nash-Sutcliffe efficiency (NSE), demonstrating an increase from 0.13 to 0.72. The incorporation of topographic static data in the coupling model was also investigated to provide the basic characteristics of the basin to the model. The results yielded an NSE exceeding 0.79. The shoreline water level was identified as a crucial input feature for indicating tidal patterns. The findings highlight the effectiveness of coupling the SWAT with LSTM for predicting tidal river flow rates, implying their applicability in similar scenarios across different basins.
Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
Study region: Chao Phraya River Basin—a major river with unique characteristics located in Thailand. Study focus: This study sought to simulate the flow rates in the Chao Phraya River Basin, which is a tidal river that poses challenges to traditional modeling approaches. The soil and water assessment tool (SWAT) is a hydrological model extensively employed for simulating flow rates. However, limitations arise in applying the SWAT model to the Chao Phraya River Basin due to its tidal nature, resulting in an unsatisfactory model performance. To address this, a long short-term memory (LSTM) model, i.e., the SWAT–LSTM model, was introduced to complement the SWAT model. New hydrological insights for the Region: The collaborative coupling of hydrological information derived from the SWAT and LSTM notably enhanced the model performance. This improvement was assessed using the Nash-Sutcliffe efficiency (NSE), demonstrating an increase from 0.13 to 0.72. The incorporation of topographic static data in the coupling model was also investigated to provide the basic characteristics of the basin to the model. The results yielded an NSE exceeding 0.79. The shoreline water level was identified as a crucial input feature for indicating tidal patterns. The findings highlight the effectiveness of coupling the SWAT with LSTM for predicting tidal river flow rates, implying their applicability in similar scenarios across different basins.
Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
Kritnipit Phetanan (author) / Seok Min Hong (author) / Daeun Yun (author) / Jiye Lee (author) / Srilert Chotpantarat (author) / Heewon Jeong (author) / Kyung Hwa Cho (author)
2024
Article (Journal)
Electronic Resource
Unknown
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Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
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