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Application of Hilbert-Huang Transform Method for Analyzing Toxic Concentrations in the Niagara River

机译:Hilbert-Huang变换方法在尼亚加拉河中有毒物质浓度分析中的应用

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摘要

A novel application of the Hilbert-Huang transform (HHT) method for the analysis of nonstationary and nonlinear time series is proposed in this paper. The HHT is introduced to the field of water quality analysis both to evaluate trends and to assess the cause-effects relationship between the system under study and external physical factors. The HHT combines two distinct analytical methods: the empirical mode decomposition (EMD) to decompose observed series into independent intrinsic mode functions and the Hilbert transform to transform these time-dependent functions into time-frequency functions. A practical application of the HHT method for data analysis is presented herein for four time series in the Niagara River: flow, water temperature, and incoming concentrations of two polycyclic aromatic hydrocarbons. It is shown that the EMD improves the spectral representation of the original time series, enabling a better detection of periodic trends and dominant time scales. It is concluded that the ability of the HHT of decomposing the signal based on the data and transforming it into the frequency-time domain provides insights into time series characteristics that could improve the modeling effort on the Niagara River.
机译:提出了一种希尔伯特-黄变换(HHT)方法在非平稳和非线性时间序列分析中的新应用。 HHT被引入水质分析领域,以评估趋势并评估所研究系统与外部物理因素之间的因果关系。 HHT结合了两种不同的分析方法:经验模式分解(EMD)将观察到的序列分解为独立的固有模式函数,希尔伯特变换则将这些与时间相关的函数转换为时频函数。本文介绍了HHT方法在尼亚加拉河中四个时间序列的数据分析的实际应用:流量,水温和两种多环芳烃的输入浓度。结果表明,EMD改善了原始时间序列的频谱表示,从而可以更好地检测周期性趋势和主要时间标度。结论是,HHT基于数据分解信号并将其转换到频率-时域的能力提供了对时间序列特征的洞察力,可以改善尼亚加拉河的建模工作。

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