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Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and Artificial Fish Swarm Algorithm

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  • Miao Yu
  • Dong Liu
  • Jean Dieu Bazimenyera

Abstract

Due to the increasing high rate of economic development and population, groundwater resources of Jiansanjiang in China are affected by both anthropogenic and natural factors. In order to achieve optimal local allocation of water resources and promotion of local economic development, a suitable method for measuring the complexity of groundwater resources system is very important. In this paper, time series fractal dimension based on the curve length calculation combined with the artificial fish algorithm for the intelligent efficient fitting of data were applied to diagnose groundwater sequence in Jiansanjiang. Fractal dimension values of monthly groundwater depth series in 15 districts of Jiansanjiang Branch Bureau and their average complexity were calculated and the results revealed that the complexity of north district is the highest while that of southern district is the lowest. Our analysis also revealed that the most important influencing factor of local groundwater depth dynamics variation is the human activities and results confirmed that combined fractal theory and artificial fish algorithm for extraction hydrological time series complexity feature is feasible and can be applied in studying regional hydrological process. It also provided a scientific basis for achieving sustainable utilization of the regional groundwater resource. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:7:p:1897-1911
    DOI: 10.1007/s11269-013-0257-8
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    Cited by:

    1. Dong Liu & Mingjie Luo & Qiang Fu & Yongjia Zhang & Khan M. Imran & Dan Zhao & Tianxiao Li & Faiz M. 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.
    2. 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.

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