长江科学院院报 ›› 2018, Vol. 35 ›› Issue (10): 1-9.DOI: 10.11988/ckyyb.20180619

• 专家特约稿 • 上一篇    下一篇

长江上游径流混沌动力特性及其集成预测研究

周建中a,b, 彭甜a,b   

  1. 华中科技大学 a.水电与数字化工程学院;
    b.数字流域科学与技术湖北省重点实验室,武汉 430074
  • 收稿日期:2018-06-19 出版日期:2018-10-01 发布日期:2018-10-22
  • 通讯作者: 彭 甜(1991-),女,湖北仙桃人,博士研究生,主要从事水文预报及水文分析研究。E-mail:husthydropt@126.com
  • 作者简介:周建中(1959-),男,湖北武汉人,教授,硕士,博士生导师,主要从事水资源利用研究。E-mail:jz.zhou@hust.edu.cn
  • 基金资助:
    国家自然科学基金重大研究计划重点支持项目(91547208);国家自然科学基金面上项目(51579107);国家重点研发计划课题(2016YFC0402708, 2016YFC0401005)

Chaotic Dynamic Characteristics and Integrated Prediction of Runoff in the Upper Reaches of Yangtze River

ZHOU Jian-zhong1,2, PENG Tian1,2   

  1. 1.School of Hydropower and Information Engineering, Huazhong University of Science and Technology,Wuhan 430074, China;
    2. Hubei Key Laboratory of Digital Valley Science and Technology,HuazhongUniversity of Science and Technology, Wuhan 430074, China
  • Received:2018-06-19 Online:2018-10-01 Published:2018-10-22

摘要: 针对长江上游干流主要站点月径流时间序列强非线性和非平稳特征,引入混沌理论和AdaBoost.RT集成极限学习机方法对其月径流时间序列进行分析和预测。首先,以流域径流非线性动力系统混沌特征参数辨识为切入点,研究并发现了流域内在特性作用下月径流时间序列动力响应的混沌现象,推求了月径流时间序列相空间重构的延迟时间和最佳嵌入维数,在此基础上,以重构相空间时间序列作为输入变量,引入基于自适应动态阈值的改进AdaBoost.RT算法改进极限学习机模型的学习性能,得到最佳的混沌集成学习月径流时间序列预测模型。实例研究结果表明,所提方法和模型能够显著提高单一极限学习机模型的泛化性和稳定性,从而获得更优越的预报性能。

关键词: 径流预报, 长江上游, 混沌动力特性, 相空间重构, 极限学习机, 集成预测

Abstract: In view of the strong nonlinearity and non-stationarity of monthly runoff in the upper reaches of Yangtze River, a hybrid model integrating the chaos theory and an ensemble AdaBoost.RT extreme learning machine is proposed for monthly runoff analysis and prediction. Firstly, the chaotic characteristics of monthly runoff in watershed were researched and revealed based on parameter identification of the runoff system. The optimal delay time and embedding dimension of the monthly runoff time series are deduced. Secondly, with the time series of the reconstructed phase space matrix as input variables, an improved AdaBoost. RT algorithm based on self-adaptive dynamic threshold was incorporated to improve the performance of extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly runoff prediction was obtained. Results showed that the proposed model could evidently improve the generalization and stability of single extreme learning machine model, and thus achieve better prediction performance.

Key words: runoff forecasting, upper reaches of Yangtze River, chaotic dynamic characteristics, phase space reconstruction, extreme learning machine, integrated prediction

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