基于多模型随机组合的水文集合预报方法研究

准确、可靠的水文预报是水资源开发利用的基础.集合预报以概率或区间的形式表征预报的不确定性,是未来水文预报研究的重点发展方向.本文提出了一种基于多模型随机组合的水文集合预报方法.首先通过加权形式将多种预报模型进行组合;再采用多目标优化算法率定各成员模型权重的上、下限;最后在优化的上、下限内随机生成权重以构建集合预报.以汉...

Hydrological ensemble forecasting method based on stochastic combination of multiple models

Accurate and reliable hydrological forecasting plays an important role in water resources development and utilization. Ensemble forecasting could characterize forecast uncertainty in the form of probability distributions or intervals, which is a key issue in hydrological forecasting. In this paper, we propose a new hydrological ensemble forecasting method, namely a stochastic combination of multiple models(SCMM) that integrates several hydrological models together with linear weights and then optimizes the upper and lower limits of the weights using a multi-objective evolutionary algorithm. Finally, it creates ensemble forecasting samples through stochastically generating the weights within the optimized interval. In a case study of the medium-long-term runoff forecasting of the Huangjinxia reservoir located on the Han River, we construct six statistical forecast models considering two lead times of a month and ten days, and optimize the limits of the weights using the improved nondominated sorting genetic algorithm(NSGA-Ⅱ) algorithm, yielding the ensemble forecasting samples. Results show that our method can better reflect the forecast uncertainty and improve significantly the average forecasts over those of the Bayesian model averaging method or the best deterministic forecast method, thus providing a promising hydrological forecasting technique.