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首页> 外文学位 >Decomposition of spring time series by discrete wavelet transform for characterization of fractured rock aquifers and hydrologic forecasting with artificial neural networks.
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Decomposition of spring time series by discrete wavelet transform for characterization of fractured rock aquifers and hydrologic forecasting with artificial neural networks.

机译：利用离散小波变换分解春季时间序列，以表征裂隙含水层，并利用人工神经网络进行水文预报。

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Characterization of a fractured rock aquifer, from which a spring emanates, represents an inverse (system identification) problem. The aquifer or system may be characterized by analyzing the output (spring discharge) using digital signal processing techniques. Spring data from eight sites were decomposed by discrete wavelet transform (DWT). For one-dimensional discretely sampled data, the DWT was implemented as a hierarchical filter bank producing wavelet (detail) coefficients that represent variation (energy) of the output signal in a time-scale plane. Two versions of the DWT were used employing Haar-2 and Daubechies-4 wavelets. Wavelet spectra, the sum of squared detail coefficients for each time scale, indicated multiple variance changes and characteristic time scales influencing variability. Aquifer characterization or classification can proceed directly from examination of normalized multi-scale energy (NME) or by training and testing an artificial neural network (ANN) with wavelet energy norms. The combination of DWT and ANN methodologies provided greater utility and flexibility for system identification than either linear (time-invariant) kernel functions or frequency domain analysis used in previous studies of fractured rock aquifer/spring systems. One spring site having a long-term discharge record was selected for hydrologic forecasting by ANN. Trials were performed with three types of delay vectors as input, namely lagged versions of mean daily (1) flow, (2) flow and precipitation, and (3) flow and estimated available percolation water. A hydrologic budget algorithm was developed to compute daily recharge. A feed forward neural network with error backpropagation conducted the nonlinear mapping between input and output for 1-day ahead predictions. An adaptation of a globally recurrent neural network for hydrologic forecasting was also tested. Ancillary investigations completed as part of this study included data compression and reconstruction by wavelets and Fourier analysis to study the timescales characterizing groundwater discharge at a selected spring, and phase space reconstruction by delay coordinate embedding.; The contribution of this research extends the state of the art in spring time series analysis and characterization techniques for fractured rock aquifers.
机译：从中散发出弹簧的裂隙含水层的表征代表了一个反问题（系统识别）。可以通过使用数字信号处理技术分析输出（弹簧流量）来表征含水层或系统。来自八个站点的春季数据通过离散小波变换（DWT）进行分解。对于一维离散采样数据，DWT被实现为分层滤波器组，该滤波器组产生小波（细节）系数，该系数表示时间尺度平面中输出信号的变化（能量）。使用Haar-2和Daubechies-4小波的两种DWT版本。小波谱是每个时间尺度的平方细节系数的总和，表示多个方差变化和影响变化性的特征时间尺度。含水层的表征或分类可以直接从标准化多尺度能量（NME）的检查中进行，也可以通过训练和测试具有小波能量范数的人工神经网络（ANN）来进行。 DWT和ANN方法学的结合比以前的裂缝性含水层/弹簧系统研究中使用的线性（时不变）核函数或频域分析为系统识别提供了更大的实用性和灵活性。 ANN选择了一个具有长期流量记录的春季站点进行水文预报。使用三种类型的延迟向量作为输入进行试验，即平均每日（1）流量，（2）流量和降水以及（3）流量和估计可用渗滤水的滞后版本。开发了水文预算算法来计算每日补给量。具有错误反向传播的前馈神经网络在输入和输出之间进行了非线性映射，用于提前1天的预测。还测试了全球递归神经网络对水文预报的适应性。作为该研究的一部分，完成的辅助研究包括通过小波进行的数据压缩和重建以及通过傅立叶分析研究表征选定春季地下水排放的时间尺度，以及通过延迟坐标嵌入进行相空间重建。这项研究的贡献扩展了裂隙含水层的春季时间序列分析和表征技术的最新技术水平。

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