基于贝叶斯自适应的水文预测应用与研究OA

To address the decline in accuracy of traditional hydrological models under climate-change-induced regime shifts in arid and semi-arid regions,a Bayesian adaptive hydrological prediction framework was developed."Historical baseline-real-time feedback-climate scenarios"were integrated as three-dimensional inputs.Outliers were filtered using the 3σ criterion,missing values were filled using an improved Kalman filter,and Z-score normalization was applied.A Bayesian LSTM core was constructed,in which Gaussian parameter modeling,MCMC prior calibration,and variational posterior inference enabled dynamic parameter adaptation and uncertainty quantification.An adaptive algorithm driven by both climate trends and prediction errors optimized drought-response parameters such as soil wilting coefficients,producing multi-scale runoff forecasts(with 95％confidence intervals),soil-moisture categories and graded drought warnings.Case studies in the Weihe River Basin show daily runoff NSE values above 0.85,drought-warning accuracy of 82％,and parameter-update response times ≤15 min,supporting refined water-resource scheduling.