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The study aims to enhance runoff prediction accuracy in hydrological modeling using a hybrid SWAT-LSTM framework and SHAP analysis.

April 19, 2026Open Access

Dominant Factor Analysis and Threshold Inflection Point Determination in Deep Learning-Based SWAT-LSTM Training Models with SHAP Interpretability Analysis

Key Points

The study aims to enhance runoff prediction accuracy in hydrological modeling using a hybrid SWAT-LSTM framework and SHAP analysis.
Developed a hybrid SWAT-LSTM framework combining hydrological and meteorological data.
Applied SHAP analysis for interpretability of dominant factors in runoff variability.
Tested the model in the Huolin River Basin, comparing performance with traditional SWAT modeling.
The hybrid model demonstrated superior performance (NSE: 0.876 vs. 0.710) in predicting runoff events.
SHAP analysis highlighted precipitation and minimum temperature as dominant factors controlling runoff variability.
Model projections indicate a non-stationary runoff pattern, with a decline in mid-century followed by an increase later.

Abstract

Climate change has intensified extreme hydrological risks, particularly in basins characterized by frequent seasonal streamflow interruptions and discontinuous hydrological records, where traditional process-based models exhibit limited capability for adaptive water resource management. This study develops a hybrid SWAT-LSTM framework that integrates SWAT-derived hydrological variables with meteorological factors and applies SHAP interpretability analysis to quantify dominant drivers and identify threshold inflection points of runoff variability. Using the upper and middle reaches of the Huolin River Basin as a case study, the coupled model outperformed the standalone SWAT model during the test period (NSE: 0.876 vs. 0.710; R2: 0.884 vs. 0.736) and more accurately reproduced extreme flood and drought events. Future projections (2026–2100), driven by the optimized FGOALS-g3 climate model under SSP2-4.5 and SSP5-8.5 scenarios, indicate increasing precipitation, accelerated minimum temperature rise, and a non-stationary runoff pattern characterized by a mid-century decline followed by a late-century increase. The SHAP results reveal strengthened meteorological dominance, particularly for precipitation and minimum temperature, while soil moisture, evapotranspiration, and percolation remain key hydrological controls. The upward shift in the minimum temperature threshold reflects strengthened temperature control on runoff dynamics under warming. The proposed framework improves extreme runoff prediction and provides a quantitative basis for climate-adaptive basin management.

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