| 基于支持向量机模型的湘江枯水预报研究 |
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| 引用本文： | 石月珍,徐冬梅. 基于支持向量机模型的湘江枯水预报研究[J]. 水利水电技术, 2011, 42(4) |
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| 作者姓名： | 石月珍 徐冬梅 |
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| 作者单位： | 长沙理工大学,水利工程学院,湖南,长沙,410114;湖南省水沙科学与水灾害防治重点实验室,湖南,长沙,410114 |
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| 基金项目： | 水沙科学与水灾害防治湖南省重点实验室 |
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| 摘 要： | 随着枯水期水资源短缺问题日益突出,人们对枯水径流的研究也越来越重视.运用支持向量机模型对湘江湘潭站年最小7 d平均流量进行预测.为了检测预报效果,将其预报结果与投影寻踪模型、人工神经网络模型的预报结果进行比较,表明支持向量机模型的误差合格率最高,预报精度也最高.
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| 关 键 词： | 支持向量机 预报精度 枯水预报 湘江 |
Support vector machine model based study OH low-water forecast of Xiangjiang River |
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| SHI Yuezhen,XU Dongmei. Support vector machine model based study OH low-water forecast of Xiangjiang River[J]. Water Resources and Hydropower Engineering, 2011, 42(4) |
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| Authors: | SHI Yuezhen XU Dongmei |
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| Affiliation: | SHI Yuezhen1,2,XU Dongmei1,2(1.Changsha University of Science & Technology,Changsha 410114,Hunan,China,2.Hunan Province Key Laboratory of Water,Sediment Sciences & Flood Hazard Prevention,China) |
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| Abstract: | Along with the increasing problem of water shortage during the dry period,more and more attentions are paid upon the study of low-water runoff.The minimum annual mean flow rate/ 7days of Xiangtan Hydrological Station of Xiangjiang River is forecasted herein based on the support vector machines model.In order to check the forecast effect,the forecast result is compared with the forecast results from both the projection pursuit model and the artificial neural networks model.It is indicated that the qualified ... |
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| Keywords: | support vector machine forecast precision low-water forecast Xiangjiang River |
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