基于LSTM的洪水预报残差修正方法研究OA

Flood forecasting for small mountainous catchments presents a significant challenge within hydrological science.Conceptual hydrological models,typified by the Xin'anjiang(XAJ)model,often struggle to accurately capture the intricate runoff generation and routing processes in mountainous terrain due to their simplified structures,frequently resulting in suboptimal forecasting accuracy.While data-driven models,such as Long Short-Term Memory(LSTM)networks,exhibit powerful fitting capabilities,their inherent"black-box"nature provides limited support from physical mechanisms.To overcome these limitations,this study develops a hybrid physics-data model that leverages an LSTM network to correct the forecast residuals of the XAJ model and incorporates SHAP for an interpretable attribution analysis.The model was validated using 15 flood events from 2015 to 2018 in the Qiaodong Village catchment,a typical small mountainous watershed in Zhejiang Province.Results indicate the following:① All XAJ-LSTM hybrid models demonstrated a significant improvement over the standalone XAJ model,which served as a baseline.The configuration that integrated the XAJ-forecasted discharge and soil moisture state variables yielded the optimal performance,with the Nash-Sutcliffe efficiency coefficient markedly increasing from 0.55 to 0.77.② The introduction of more information does not necessarily enhance model performance;supplementing the optimal configuration with additional runoff component data resulted in performance degradation due to informational redundancy.③ From a mechanistic perspective,the SHAP analysis confirmed that antecedent observed discharge and XAJ-forecasted discharge are the key drivers influencing the residual correction model's decisions.This reveals that the integration of physical information effectively guides the data-driven model's learning process,shifting its focus from mere statistical fitting to reliance on more robust physical constraints.The proposed physics-data hybrid model,which combines high accuracy with strong interpretability,offers a new paradigm for the development of reliable and trustworthy intelligent hydrological forecasting models.