摘要
为了建立雅鲁藏布江羊村站的洪水预报方案,对ν-支持向量机洪水过程预报模型（ν-SVR）进行了深入研究。针对羊村站以上流域洪水特征和站网分布特点,采用ν-SVR模型方法建立了该站洪水过程预报模型。选取了1998~2004年及2005-2007年汛期洪水和相应降水资料进行了模型率定和检验。结果表明,ν-SVR洪水过程预报模型能很好地控制支持向量个数,在降低模型的复杂程度的同时,能保持良好的预报精度,可用于洪水作业预报。
In order to establish the flood forecast scheme for Yangcun Station on Brahmaputra River,the flood forecast model based on the support vector machine was studied. According to the characteristics of flood and the hydrological observation station network in Brahmaputra River Basin,the flood forecast model for Yangcun Station with ν- SVR forecast model was established.The model was calibrated and verified respectively by hydrological data and precipitation data from 1988 to 2004 and 2005 to2007 in flood seasons. The results show that the flood forecast model with ν- SVR can control the number of support vectors,reduce the complexity of the model,and preserve good forecast accuracy,which can be applied in flood forecasting.
出处
《人民长江》
北大核心
2016年第16期1-4,共4页
Yangtze River
基金
国家自然科学基金项目(41371047)
关键词
洪水预报
支持向量机
BP神经网络
雅鲁藏布江
flood forecasting
support vector machine
BP neural network
Brahmaputra River
