ABSTRACT As a sudden and highly destructive geological hazard, debris flows pose significant challenges for hazard assessment. However, such assessments often suffer from sparse data, unclear mechanisms, and limited decision‐making transparency, highlighting the need for a synergistic modeling approach that integrates domain knowledge with data‐driven methods. To address this need, this study proposes a knowledge–data dual‐driven hazard assessment framework. By employing an improved AHP–CRITIC hybrid weighting scheme, it effectively combines expert knowledge with statistical characteristics of multi‐source data and incorporates the SHAP model to enhance the interpretability of the decision‐making process. Using Gaoling Gully as a case study and integrating field investigations with satellite imagery, 14 evaluation factors were selected from four categories—topography, lithology, hydrology, and land cover. An interpretable single‐gully debris‐flow hazard assessment framework was developed based on an improved AHP–CRITIC weighting method and the SHAP interpretation model. The AHP–CRITIC approach was used to combine subjective and objective weights to delineate hazard zones under different rainfall return periods (10a, 20a, 50a, and 100a). The assessment results were further validated using FLO‐2D numerical simulations and ROC curves. The results demonstrate that the AHP–CRITIC method achieves high predictive performance and shows strong agreement with the FLO‐2D simulation results, indicating a clear physical basis. As rainfall intensity increases, high‐hazard zones exhibit a linear expansion along the main gully. The SHAP analysis further reveals that hydrodynamic and source‐material conditions play dominant roles in the study area. This integrated assessment framework provides a solid theoretical foundation and technical support for managing single‐gully debris‐flow hazards.
Chen et al. (Mon,) studied this question.