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首页> 外文期刊>Water Resources Management >Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions
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Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions

机译：人工神经网络与小波人工神经网络用于不同分形维数的地下水深度数据预测的比较研究

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摘要

The objective of this study was comparative study of artificial neural networks (ANN) and wavelet artificial neural networks (WANN) for time-series groundwater depth data (GWD) forecasting with various curve fractal dimensions. The paper offered a better method of revealing the change characteristics of GWD. Time series prediction based on ANN algorithms is fundamentally difficult to capture the data change details, when the time-series GWD data changes are more complex. For this purpose, Wavelet analysis and fractal theory methods are proposed to link to ANN models in predicting GWD and analysis the change characteristics. The trend and random components were separated from the original time-series GWD using wavelet methods. The fractal dimension is convenient for quantitatively describing the irregularity or randomness of time series data. Three types of training algorithms for ANN and WANN models using a Mallat decomposition algorithm were investigated as case study at three sites in the Ganzhou region of northwest China to find an optimal model that is suitable for certain characteristics of time-series GWD data. The simulation results indicate that both WANN and ANN models with the Bayesian regularization algorithm are accurate in reproducing GWD at sites with smaller fractal dimensions. However, WANN models alone are suitable for sites at which the fractal dimension of the wavelet decomposition detail components is larger. Prediction error is also greater when the fractal dimension is larger.
机译：这项研究的目的是对人工神经网络（ANN）和小波人工神经网络（WANN）进行比较研究，以预测具有各种曲线分形维数的时间序列地下水深度数据（GWD）。本文提供了一种更好的揭示GWD变化特征的方法。当时间序列GWD数据变化更为复杂时，基于ANN算法的时间序列预测从根本上很难捕获数据变化细节。为此，提出了小波分析和分形理论方法，将其与人工神经网络模型联系起来，用于预测全球变暖率并分析其变化特征。使用小波方法将趋势分量和随机分量与原始时间序列GWD分开。分形维数便于定量描述时间序列数据的不规则性或随机性。在中国西北部赣州地区的三个地点，研究了使用Mallat分解算法对ANN和WANN模型进行的三种训练算法，以找到适合于某些时间序列GWD数据特征的最优模型。仿真结果表明，使用贝叶斯正则化算法的WANN和ANN模型在分形维数较小的位置都能精确再现GWD。但是，仅WANN模型适用于小波分解细节分量的分形维数较大的位置。分形维数越大，预测误差也越大。

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  • 来源
    《Water Resources Management》 |2014年第15期|5297-5317|共21页
  • 作者

    Zhenfang He; Yaonan Zhang; Qingchun Guo; Xueru Zhao;

  • 作者单位

    Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China,Liaocheng University, Liaocheng 252059, China,University of Chinese Academy of Sciences, Beijing 100049, China;

    Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;

    University of Chinese Academy of Sciences, Beijing 100049, China,Shaanxi Radio & TV University, Xi'an 710068, China;

    Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China,Lanzhou University, Lanzhou 730000, China;

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

    Artificial neural network; Wavelet artificial neural network; Groundwater depth; Training algorithms; Mallat decomposition algorithm; Fractal dimension;

    机译：人工神经网络;小波人工神经网络地下水深度;训练算法;Mallat分解算法;分形维数;

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