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张卫国, 钟平安, 张玉兰, 等. 季节性支持向量机中长期径流预报模型[J]. 水力发电, 2014, 40(4): 17-21. ZHANG Weiguo, ZHONG Pingan, ZHANG Yulan, et al. A seasonal support vector machine mid-long term runoff forecast model. Water Power, 2014, 40(4): 17-21. (in Chinese)

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• 标题: SSA-LSSVM在中长期径流预测中的应用研究Application of SSA-LSSVM in Mid-Long Term Runoff Prediction

  作者: 巴欢欢, 郭生练, 钟逸轩, 刘章君

  关键字: 中长期径流预测, 奇异谱分析, 季节性一阶自回归, 支持向量机, 最小二乘支持向量机, 水布垭水库Mid-Long Term Runoff Prediction, Singular Spectrum Analysis, SAR Model, SVM, LSSVM, Shuibuya Reservoir

  期刊名称: 《Journal of Water Resources Research》, Vol.5 No.5, 2016-10-10

  摘要: 为提高中长期径流预测精度，利用奇异谱分析(SSA)对输入资料进行数据预处理，消除噪声，得到重建序列。以水布垭水库1951~2009年的入库月径流资料为依据，选用季节性一阶自回归模型、支持向量机模型和最小二乘支持向量机模型作为径流预测模型，对原始序列和重建序列进行模拟预测。结果表明，基于奇异谱分析的最小二乘支持向量机的模拟预测精度最高，率定期和检验期的模型效率系数分别高达89%和84%。说明采用SSA对资料进行预处理可以显著提高中长期径流预报的精度。 To improve the accuracy of runoff prediction, Singular Spectrum Analysis (SSA) is applied to preprocess the original flow series and a new reconstructed series is obtained. The monthly inflow data of the Shui-buya Reservoir from 1951 to 2009 were selected as a case study. Seasonal Autoregressive (SAR) model, support vector machine (SVM) and least square support vector machine (LSSVM) are used to simulate and predict the original and reconstructed data series. The results show that SSA-LSSVM performs the best among these models, in which the model efficiency coefficients reach 89% and 84% during the verification and testing periods, respectively. It is shown that the accuracy of mid-long term runoff prediction can be significantly improved by using SSA.