基于多情景划分的三峡水库入库径流预报校正北大核心CSCD

本文考虑预报发生时的外界环境差异性及不同预报情景下预报误差的不同,根据降雨情况、预见期等关键影响因子的不同,实现了预报情景的划分,并进一步基于历史预报误差数据实现了不同情景下相对预报误差分布规律以及90%置信度下置信区间的推求。并基于变分模式分解和长短期记忆神经网络模型,建立了考虑预报误差和预报情景的多维、多属性径流预报校正方法,通过三峡水库实例分析发现,入库径流预报的平均相对误差由实际的8.32%降低为6.36%,减少幅度达到23.6%。此外,平均绝对误差、均方根误差及确定性系数等其他指标都得到了不同程度的改善。说明本文的方法可增加校正模型的有效信息输入,从而提高径流预报模型精度。

Correction to predicted inflow of Three Gorges Reservoir based on division of different scenarios

In this work, we consider the difference in external environments on which the forecasts are based and the difference in forecast errors under different scenarios, and work out a method for division of the prediction scenarios by the key influencing factors, such as rainfall and forecast period. Then, the data of historical forecast errors can be used to obtain their distribution trends and thus determine the confidence interval at the 90% level for different scenarios. Considering prediction scenarios, prediction errors, and their trends, we construct a new multidimensional and multi-attribute method for correcting inflow prediction, using the variable mode decomposition and a long-short term memory neural network model. Through a case study of the Three Gorges reservoir, we find the average relative error of inflow predictions is reduced from 8.32% to 6.36%, with a reduction rate of 23.6%. And other indicators are improved to varying degrees, such as average absolute error, root mean square error, and coefficient of determination. This shows our method has achieved a significant improvement on the accuracy of inflow prediction models through increasing the effective information input to the correction model.