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为提高洪水预报模型的精度和可靠性,基于贝叶斯模型平均方法(BMA),结合水动力学模型和统计相关模型,对秦淮河流域东山站水位进行多模型集合预报并进行模型率定与验证。结果表明,BMA的预报确定性系数CCE均高于水动力学模型和统计相关模型,且均方差RRSME最小;BMA法降低了单一水文预报结果的不确定性,保证洪水预报具备较高的精度,并提供了洪水水位的置信区间,为防洪规划提供了依据。
In order to improve the accuracy and reliability of the flood forecasting model, based on the Bayesian model averaging method (BMA), combined with the hydrodynamic model and statistical correlation model, the multi-model ensemble of the Dongshan water level in the Qinhuai River Basin is predicted and modeled verification. The results showed that the predictive coefficient of determinacy (CCE) of BMA was higher than that of hydrodynamic model and statistical correlation model, and the mean square error (RRSME) was the smallest. The BMA method reduced the uncertainty of single hydrological forecast result and ensured the high precision of flood forecast. And provided the confidence interval of flood water level, which provided the basis for flood control planning.