Wavelet-Based Analysis on the Complexity of Hydrologic Series Data under Multi-Temporal Scales

• Main Authors: Ling Wang, Qing-Ping Zhu, Ji-Chun Wu, Dong Wang, Yan-Fang Sang
• Format: Article
• Language: English
• Published: MDPI AG 2011-01-01
• Series: Entropy
• Subjects: time series analysis; complexity; wavelet; information theory; entropy; multi-temporal scale; noise; probability density function; trend
• Online Access: http://www.mdpi.com/1099-4300/13/1/195/

In this paper, the influence of four key issues on wavelet-based analysis of hydrologic series’ complexity under multi-temporal scales, including the choice of mother wavelet, noise, estimation of probability density function and trend of series data, was first studied. Then, the complexities of several representative hydrologic series data were quantified and described, based on which the performances of four wavelet-based entropy measures used commonly, namely continuous wavelet entropy (CWE), continuous wavelet relative entropy (CWRE), discrete wavelet entropy (DWE) and discrete wavelet relative entropy (DWRE) respectively, were compared and discussed. Finally, according to the analytic results of various examples, some understanding and conclusions about the calculation of wavelet-based entropy values gained in this study have been summarized, and the corresponding suggestions have also been proposed, based on which the analytic results of complexity of hydrologic series data can be improved.