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.
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Acknowledgment
This research is supported by the National Natural Science Foundation of China No. 41071053, Postdoctoral Science Foundation of China No.20080440832, Postdoctoral Science Foundation Special Funds of China No.201003410, Specialized Research Foundation of Colleges and Universities Doctoral Program No.20102325120009, Natural Science Foundation of Heilongjiang Province No.C201026, Science and Technology Research Project of the Education Bureau in Heilongjiang Province No.11541024, Doctoral Start-up Foundation of Northeast Agricultural University No.2009RC37.
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Yu, M., Liu, D. & de Dieu Bazimenyera, J. Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and Artificial Fish Swarm Algorithm. Water Resour Manage 27, 1897–1911 (2013). https://doi.org/10.1007/s11269-013-0257-8
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DOI: https://doi.org/10.1007/s11269-013-0257-8