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[1,2] 钟逸轩 刘章君[1,2] 

机构地区：[1]武汉大学水资源与水电工程科学国家重点实验室,湖北武汉 [2]水资源安全保障湖北省协同创新中心,湖北武汉

出　　处：《水资源研究》2016年第5期423-433,共11页Journal of Water Resources Research

基　　金：国家自然科学基金重点项目(51539009);十三五国家重点研发项目(2016YFC0402206)资助

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 siguiflcantly improved by using SSA.

关 键 词：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 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]