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Study on the Predicting Model for the Flow of Springs in Karst Water System with Support Vector Machines
Pages: 29-32,42
Year: Issue:  12
Journal: Geotechnical Investigation & Surveying

Keyword:  支持向量机 神经网络 时间序列 泉流量预报模型;
Abstract: 针对岩溶系统结构不甚清晰、基础资料不完备条件下泉水流量预报问题,引入了能较好地解决小样本、非线性、高维数和局部极小点等问题的支持向量机(Support Vector Machines,SVM)方法,将泉流量影响因子时间序列与支持向量机方法有机结合,建立了岩溶水系统支持向量机泉流量预报模型,并与BP神经网络模型进行了实例比较。结果表明,SVM模型具有泛化能力强、预报精度高的特点,可很好地克服神经网络的过学习问题,同时,针对SVM模型"峰值"预报精度差的缺点,提出了"峰值"预报解决方案。
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