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Precipitation Complexity Measurement Using Multifractal Spectra Empirical Mode Decomposition Detrended Fluctuation Analysis

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  • Dong Liu
  • Mingjie Luo
  • Qiang Fu
  • Yongjia Zhang
  • Khan Imran
  • Dan Zhao
  • Tianxiao Li
  • Faiz Abrar

Abstract

The stability of current methods of complexity measurement are generally Inefficient. In this study, multifractal spectra (MFS) analysis, which depends on empirical mode decomposition detrended fluctuation analysis (EMD–DFA), was used to measure the complexity of the monthly precipitation series from 1964 to 2013 (50 years) of 11 districts in Harbin, Heilongjiang Province, China. By comparing the anti-noise capability of MFS–EMD–DFA with that of conventional complexity measurement approaches, such as sample entropy, Lempel–Ziv complexity, and approx mate entropy, it was established that MFS–EMD–DFA has greater robustness in anti-noise jamming, and thus it could be applied more widely. The precipitation series complexity strength map of the 11 regions was drawn using a geographical information system. This study analyzed the correlation between precipitation and some meteorological factors and then ranked their strengths. The results showed that many meteorological factors have strong connections with the regional precipitation series in the study area. This study provided a solid foundation for further extraction of hydrological information in Harbin and proposed a new method for complexity analysis. The novel MFS–EMD–DFA approach could also be applied to the analysis of multifractal characteristics as well as complexity measurement in various other disciplines. Copyright Springer Science+Business Media Dordrecht 2016

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  • Dong Liu & Mingjie Luo & Qiang Fu & Yongjia Zhang & Khan Imran & Dan Zhao & Tianxiao Li & Faiz Abrar, 2016. "Precipitation Complexity Measurement Using Multifractal Spectra Empirical Mode Decomposition Detrended Fluctuation Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 505-522, January.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:2:p:505-522
    DOI: 10.1007/s11269-015-1174-9
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    References listed on IDEAS

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    1. Philip Wallis & Raymond Ison, 2011. "Appreciating Institutional Complexity in Water Governance Dynamics: A Case from the Murray-Darling Basin, Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(15), pages 4081-4097, December.
    2. Suleymanov, Arif A. & Abbasov, Askar A. & Ismaylov, Aydin J., 2009. "Fractal analysis of time series in oil and gas production," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2474-2483.
    3. Qian, Xi-Yuan & Gu, Gao-Feng & Zhou, Wei-Xing, 2011. "Modified detrended fluctuation analysis based on empirical mode decomposition for the characterization of anti-persistent processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4388-4395.
    4. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.
    5. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    6. Benoit Mandelbrot, 1999. "Survey of Multifractality in Finance," Cowles Foundation Discussion Papers 1238, Cowles Foundation for Research in Economics, Yale University.
    7. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    8. Miao Yu & Dong Liu & Jean Dieu Bazimenyera, 2013. "Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and Artificial Fish Swarm Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1897-1911, May.
    9. Xiaohui Yuan & Bin Ji & Hao Tian & Yuehua Huang, 2014. "Multiscaling Analysis of Monthly Runoff Series Using Improved MF-DFA Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3891-3903, September.
    10. Breslin, M.C. & Belward, J.A., 1999. "Fractal dimensions for rainfall time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(4), pages 437-446.
    11. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3218-3229.
    12. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    13. Rizvi, Syed Aun R. & Dewandaru, Ginanjar & Bacha, Obiyathulla I. & Masih, Mansur, 2014. "An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 86-99.
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    1. Muhammad Imran Khan & Dong Liu & Qiang Fu & Shuhua Dong & Umar Waqas Liaqat & Muhammad Abrar Faiz & Yuxiang Hu & Qaisar Saddique, 2016. "Recent Climate Trends and Drought Behavioral Assessment Based on Precipitation and Temperature Data Series in the Songhua River Basin of China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4839-4859, October.
    2. Qiang Fu & Ye Liu & Tianxiao Li & Dong Liu & Song Cui, 2017. "Analysis of Irrigation Water Use Efficiency Based on the Chaos Features of a Rainfall Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1961-1973, April.
    3. Hasan Tatli & H. Nüzhet Dalfes, 2020. "Long-Time Memory in Drought via Detrended Fluctuation Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1199-1212, February.

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