A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting | JoVE Visualize

Area of Science:

Hydrology and Water Resources
Environmental Modeling
Artificial Intelligence in Environmental Science

Background:

Accurate extreme streamflow simulation is critical for flood forecasting, water management, and water quality.
Existing process-based and data-driven models face limitations in accuracy, robustness, and interpretability, especially during extreme events.
Addressing these challenges requires advanced modeling techniques for reliable hydrological predictions.

Purpose of the Study:

To develop and evaluate a novel hybrid modeling framework for enhanced streamflow prediction.
To integrate a process-based model (SWAT) with an optimized deep learning approach (BiLSTM) and an interpretability tool (SHAP).
To improve the accuracy, stability, and interpretability of streamflow simulations, particularly under extreme hydrological conditions.

Main Methods:

A hybrid framework integrating the Soil and Water Assessment Tool (SWAT) with a Bidirectional Long Short-Term Memory (BiLSTM) network.
Bayesian optimization (BO) for optimizing the BiLSTM network and random forest (RF) with correlation analysis for feature selection.
SHapley Additive Explanations (SHAP) for analyzing model behavior and providing interpretable insights into hydrological processes.

Main Results:

The coupled models significantly outperformed standalone models, with R² and Nash-Sutcliffe Efficiency (NSE) improving by 14.7%-27.1% and 10.0%-35.0%, respectively.
The SWAT-S-BiLSTM model demonstrated superior performance, achieving R² of 0.89 and NSE of 0.81.
Extreme flow prediction showed substantial improvement, with relative error for the top 0.5% of flows reducing from -11.72% (SWAT) to -0.27% (SWAT-S-BiLSTM).
SHAP analysis revealed hydrological lag effects, threshold behaviors, and nonlinear responses, clarifying how the hybrid model corrects physical model deficiencies.

Conclusions:

The proposed hybrid framework effectively enhances streamflow prediction accuracy and stability, especially for extreme events.
The integration of deep learning with physical models and interpretability tools offers a robust solution for hydrological forecasting.
The framework provides valuable insights into hydrological processes and demonstrates significant potential for practical applications in water resource management.