| 基于遗传熵谱估计的年径流周期识别 |
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| 引用本文： | 张明,张建云,金菊良.基于遗传熵谱估计的年径流周期识别[J].水科学进展,2009,20(3):337-342. |
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| 作者姓名： | 张明 张建云 金菊良 |
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| 作者单位： | 1.河海大学水文水资源与水利工程科学国家重点实验室, 江苏, 南京, 210098; |
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| 基金项目： | 公益性行业科研专项基金，国家自然科学基金，国家科技支撑计划重点项目，中国气象局成都高原气象开放实验室基金 |
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| 摘 要： | 为识别年径流量序列的隐含周期,提出基于加速遗传算法的熵谱估计算法,与传统的方差谱和Burg谱相比,该方法由熵谱分析的4个等价条件构建多目标函数,并以加速遗传算法作为优化算法,谱估计结果不依赖于初始值的选取,对数据长度、信噪比和初相位有较强的适应性。在三川河流域后大成站1956-2000年径流量序列周期识别中的应用结果表明,在95%的置信检验水平下,序列中存在着12.29年和2.67年的显著隐含周期,为三川河流域年径流的变化规律和变化的阶段性研究提供了一条新的定量研究手段。
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| 关 键 词： | 年径流 时间序列 周期识别 最大熵谱估计 加速遗传算法 |
| 收稿时间： | 2008-08-30 |
Genetic entropy spectral estimation method and its application to annnual runoff periodic identification |
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| ZHANG Ming,ZHANG Jian-yun,JIN Ju-liang.Genetic entropy spectral estimation method and its application to annnual runoff periodic identification[J].Advances in Water Science,2009,20(3):337-342. |
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| Authors: | ZHANG Ming ZHANG Jian-yun JIN Ju-liang |
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| Institution: | 1.State Key Laboratory of Hydrology-water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2.Research Center for Climate Change, MWR, Nanjing 210029, China;3.Nanjing Hydraulic Research Institute, Nanjing 210029, China;4.College of Civil Engineering, Hefei University of Technology, Hefei 230009, China |
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| Abstract: | An improved entropy spectral estimation method,Genetic Entropy Spectral estimation(GES),is proposed to identify the implicit periods in annual runof time series.The method is based on the accelerating genetic algorithm(AGA),which is mainly used to optimize the parameters of maximum entropy spectral analysis method(MESA),and minimize the four equivalent conditions of MESA.Compared to the traditional variation spectral method and Burg spectral method,the entropy estimation results based on the improved method is not depend on the selection of initial value,further more,the method has high adaptability for data length,signal noise ratio and initial phases.Taking Houdacheng station in the Sanchuanhe River basin as a case,an annual runof series from 1956 to 2000 is studied with the method.And results show that there are two prominent periods of 12.29 years and 2.67 years in the time series with 95% confidence level.GES method can provide a new approach for variation law and phases analysis study of runof series. |
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