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Wavelet-Based Hydrological Time Series Forecasting

机译:基于小波的水文时间序列预测

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摘要

These days wavelet analysis is becoming popular for hydrological time series simulation and forecasting. There are, however, a set of key issues influencing the wavelet-aided data preprocessing and modeling practice that need further discussion. This article discusses four key issues related to wavelet analysis: discrepant use of continuous and discrete wavelet methods, choice of mother wavelet, choice of temporal scale, and uncertainty evaluation in wavelet-aided forecasting. The article concludes with a personal reflection on solving the four issues for improving and supplementing relevant wavelet studies, especially wavelet-based artificial intelligence modeling.
机译:如今,小波分析在水文时间序列模拟和预测中正变得越来越流行。但是,存在一系列影响小波辅助数据预处理和建模实践的关键问题,需要进一步讨论。本文讨论了与小波分析有关的四个关键问题:连续小波方法和离散小波方法的不正确使用,母子波的选择,时间尺度的选择以及小波辅助预测中的不确定性评估。本文最后总结了个人对解决四个问题的个人思考,以改进和补充相关的小波研究,尤其是基于小波的人工智能建模。

著录项

  • 来源
    《Journal of hydrologic engineering》 |2016年第5期|06016001.1-06016001.5|共5页
  • 作者单位

    Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

    Caroline and William N. Lehrer Distinguished Chair in Water Engineering, Dept. of Biological and Agricultural Engineering and Zachry Dept. of Civil Engineering, Texas A&M Univ., 321 Scoates Hall, 2117 TAMU, College Station, TX 77843-2117;

    Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China;

    Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Hydrological forecasting; Artificial intelligence modeling; Wavelet analysis; Temporal scale; Hydrological time series analysis; Statistical significance;

    机译:水文预报;人工智能建模;小波分析;时间尺度;水文时间序列分析;统计学意义;

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