基于新安江模型-双向长短期记忆网络与SHAP归因的阜平流域洪水预报残差修正研究OA

To address the issues of flood forecasting for flood-peak underestimation in traditional conceptual models and insufficient physical constraints in data-driven models for semi-arid and semi-humid catchments in northern China,a residual correction model for flood forecasting was developed by coupling the Xin'anjiang model(XAJ)with a bidirectional long short-term memory network(BiLSTM)for the Fuping Catchment in the Daqing River subsystem of the Haihe River basin.The SHAP method was introduced to interpret the model's compensation mechanism.Physical variables derived from XAJ,including forecasted discharge,three-layer soil moisture,and runoff components,were used as inputs.A progressive feature combination approach was adopted to construct the BiLSTM residual correction model and to evaluate the effects of different physical information on forecasting performance.The results indicate that XAJ exhibits noticeable flood-peak underestimation and temporal deviation in the Fuping catchment.When the baseline forecasted discharge from XAJ is used as a prior input to BiLSTM,the hybrid model achieves significantly improved overall fitting performance,and the physically constrained framework effectively suppresses numerical distortions in purely data-driven models under extreme conditions.After further incorporating the three-layer soil moisture,the model captures flood peaks most accurately,with the mean relative error of flood peaks reduced to within 10%.SHAP attribution analysis reveals that soil moisture state variables are key drivers of positive residual compensation.Runoff component variables do not provide stable additional gains under the current sample conditions,and their inclusion may weaken the model's generalization ability due to error propagation and information redundancy.The research reveals that the proposed XAJ-BiLSTM hybrid framework,constrained by key physical state variables,effectively improves flood-peak forecasting accuracy in complex catchments in northern China and provides a reference for the interpretable construction of physics-data fusion hydrological models.