基于支持向量回归机的长期径流预报及不确定性分析

被引：14

作者：

郦于杰

梁忠民

唐甜甜

机构：

[1] 河海大学水文水资源学院

来源：

南水北调与水利科技 | 2018年 / 16卷 / 03期

关键词：

汉江流域; 长期径流预报; 支持向量回归机; 遗传算法; 贝叶斯概率预报;

D O I：

10.13476/j.cnki.nsbdqk.2018.0067

中图分类号：

P338 [水文预报];

学科分类号：

摘要：

根据汉江流域皇庄站1981-2008年逐月径流量与1980-2007年逐月74项环流指数、北太平洋海温场、500hPa高度场的相关关系,利用逐步回归挑选预报因子,构建基于遗传算法的支持向量回归机模型(GA-SVR),并对2009-2013年逐月径流量进行预报;结果表明,径流预报精度较高,汛期平均相对误差在30%以内,非汛期、年总量平均相对误差在20%以内,均优于随机森林和多元线性回归模型。将GA-SVR模型的预报结果作为概率预报的基础,采用贝叶斯理论中的水文不确定性处理器(HUP)对预报的可靠度进行分析;结果表明,HUP不仅可以提供精度更高的定值预报,还能以置信区间的方式量化预报的可靠度,提供更为丰富的预报信息。

引用

页码：45 / 50

页数：6

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