Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning DOI Open Access
Jimin Lee, Jeongho Han, Bernard A. Engel

и другие.

Environments, Год журнала: 2025, Номер 12(3), С. 94 - 94

Опубликована: Март 17, 2025

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation effective watershed management for sustainable water resource use. Soil Water Assessment Tool (SWAT) is widely utilized modeling but shows limitations in simulation its uniform application the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial temporal variability. To address these challenges, this study integrated SWAT with Tree-Based Pipeline Optimization (TPOT), an automated machine learning (AutoML) framework, predict HRU-specific factors. Furthermore, a user-friendly web-based program was developed improve accessibility practical optimized factors, supporting more predictions, even ungauged watersheds. proposed approach area significantly enhanced recession predictions compared traditional method. This improvement supported by key performance metrics, including Nash–Sutcliffe Efficiency (NSE), coefficient determination (R2), percent bias (PBIAS), mean absolute percentage error (MAPE). framework effectively improves accuracy practicality modeling, offering scalable innovative solutions face stress.

Язык: Английский

Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning DOI Open Access
Jimin Lee, Jeongho Han, Bernard A. Engel

и другие.

Environments, Год журнала: 2025, Номер 12(3), С. 94 - 94

Опубликована: Март 17, 2025

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation effective watershed management for sustainable water resource use. Soil Water Assessment Tool (SWAT) is widely utilized modeling but shows limitations in simulation its uniform application the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial temporal variability. To address these challenges, this study integrated SWAT with Tree-Based Pipeline Optimization (TPOT), an automated machine learning (AutoML) framework, predict HRU-specific factors. Furthermore, a user-friendly web-based program was developed improve accessibility practical optimized factors, supporting more predictions, even ungauged watersheds. proposed approach area significantly enhanced recession predictions compared traditional method. This improvement supported by key performance metrics, including Nash–Sutcliffe Efficiency (NSE), coefficient determination (R2), percent bias (PBIAS), mean absolute percentage error (MAPE). framework effectively improves accuracy practicality modeling, offering scalable innovative solutions face stress.

Язык: Английский

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