水力发电学报2017，Vol.36Issue(10)：45-55,11.DOI:10.11660/slfdxb.20171005
基于小波支持向量机的径流预测性能优化分析
Performance optimization analysis for inflow prediction using wavelet Support Vector Machine
摘要
Abstract
Mid-long term inflow prediction is a critical prerequisite and complicated issue for reservoir operation.This paper presents a wavelet decomposition parameter optimized support vector machine (WD-SVM-PSO) model based on previous studies of the data driven prediction theory,including historical inflows frequency division pre-process,classification based training,parameter optimization and cross validation.Its performance is optimized in terms of dataset refining,model parameters calibration,and training mechanism.Application to the annual inflows of the Xianghongdian reservoir in the Huai River basin during 1959-2014 shows that 93％ of its predictions are acceptable due to its better generalization performance and it can significantly reduce the overfitting.And the controlled trial simulation reveals the effect of three key elements,ranked from top to down:data set pre-process,prediction model,model parameters.This study helps analyze and improve data driven prediction models and theft accuracy and reliability of inflow prediction.关键词
年径流预测/小波分解/支持向量机/性能优化分析/响洪甸水库Key words
annual runoff prediction/wavelet decomposition/Support Vector Machine/performance optimization analysis/Xianghongdian reservoir分类
建筑与水利引用本文复制引用
周婷,金菊良,李荣波,纪昌明,李继清..基于小波支持向量机的径流预测性能优化分析[J].水力发电学报,2017,36(10):45-55,11.基金项目
国家自然科学基金(51509001) （51509001）
安徽省自然科学基金(1608085QE112) （1608085QE112）
国家重点研发计划项目(2016YFC0401305 （）
2016YFC0402208) （）
安徽省高校优秀青年人才支持计划项目(gxyqZD2017019) （gxyqZD2017019）
