Abstract
The present scholars in the study of the evolution of a regional groundwater resource system tend to ignore the complexity of the system itself, making it difficult to truly realize the scientific management of groundwater resources. Thus, the quantization characteristics of the complexity of the regional groundwater resource system have become a hot issue concerned in the field of hydrology. In this paper, taking Jiansanjiang Administration of Agricultural Reclamation, Heilongjiang, China for example, a wavelet entropy method is used for diagnosing each of its monthly groundwater depth sequence complexity to determine the order of the complexity, thus calculating the averaged wavelet entropy of the monthly groundwater depth sequence for each zone and revealing the local monthly groundwater depth sequence complexity has an obvious regional characteristic gradually decreased from north to south. Through comparative analysis of three kinds of complexity measure algorithms, including wavelet entropy, multi-scale semi-square difference dimension, and sample entropy, we find that the diagnostic result of the complexity of the wavelet entropy algorithm has sufficient visibility and high operational efficiency, so that it is an effective way to measure the hydrological sequence complexity. The results of the analysis of the cause of the local monthly groundwater depth sequence complexity show that: changes in precipitation and agricultural production activities are the critical driving factors for the dynamic changes in the local groundwater depth sequence. Research result reveals the rules of the spatial variation on the local groundwater depth complexity and provides a research model for the research of the process complexity of regional hydrology as well as a scientific basis on zone management and sustainable use of regional groundwater resources.
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References
Daqrouq K (2011) Wavelet entropy and neural network for text-independent speaker identification. Eng Appl Artif Intell 24(5):796–802
Guo L, Ma MK, Zhang Y (2009) Landscape assessment on wetland degradation during thirty years in Jiansanjiang region of Sanjiang Plain, Northeast China. Acta Ecologica Sinica 29:3126–3135
He ZY, Gao SB, Chen XQ et al (2011) Study of a new method for power system transients classification based on wavelet entropy and neural network. Int J Electr Power Energy Syst 33(3):402–410
Huang F, Xia Z, Li F et al (2013) Assessing sediment regime alteration of the upper Yangtze River. Environ Earth Sci 70(5):2349–2357
Li RZ, Qian JZ (2005) Unascertained risk analysis of groundwater level drop. Scientia Geographica Sinica 25:631–635
Li XB, Ding J, Li HQ (1999) Wavelet analysis of hydrological time series. Adv Water Sci 10:144–149
Li BJ, Zhu CY, Zhou J (2002) Effects of non-dimensional quantities of original data on grey incidence order. J Henan Agric Univ 2:199–202
Li JX, Ke XZ, Guo H (2007) The application of wavelet variance and wavelet entropy in signal feature extraction. J Xi’an Univ Technol 23(4):365–369
Liu D, Yu M (2013) Groundwater resources complexity diagnosis based on multiple-scale semivariance fractal dimension. Int J Adv Comput Technol 5(1):112–119
Liu XH, Jiang YT, Zhao YF (2006) Sustainable utilization mode and sustainable degree of groundwater resources in north. Water Resour Hydropower Northeast China 24(37–38):42
Liu M, Hang LH, Shen B (2009) Physical influencing factors of groundwater depth in Hotan Oasis. Chin J Eco Agric 17:174–177
Miao C, Yang L, Liu B (2011) Stream flow changes and its influencing factors in the mainstream of the Songhua River basin, Northeast China over the past 50 years. Environ Earth Sci 63(3):489–499
Morlet J, Arens G, Fourgeau E, Giard D (1982) Wave propagation and sampling theory—Party I: complex signal and scattering in multilayered media. Geophysics 47(2):203–221
Peng J, Shen H, Wu JS (2013) Soil moisture retrieving using hyperspectral data with the application of wavelet analysis. Environ Earth Sci 69(1):279–288
Safty SE, El-Zonkoly A (2009) Applying wavelet entropy principle in fault classification. Int J Electr Power Energy Syst 31(10):604–607
Sello S (2000) Wavelet entropy as a measure of solar cycle complexity. Astron Astrophys 363:311–315
Shi JF, Zhang FJ, Hao BF (2009) A method of image segment based on wavelet transform and mathematical morphology. J Taiyuan Univ Technol 40:490–493
Tian GZ (2007) Study on dynamic groundwater level and its effect in Changpin district. Beijing Water 5:44–47
Wang WS, Ding J, Li YQ (2005a) Hydrological wavelet analysis. Chemical Industry Press, Peking
Wang WS, Man WJ, Ding J (2005b) Study on the complexity of runoff change based on the dis-noising of wavelet transform and symbolic dynamics. Adv Water Sci 16:380–383
Widodo A, Shim MC, Caesarendra W et al (2011) Intelligent prognostics for battery health monitoring based on sample entropy. Expert Syst Appl 38(9):11763–11769
Wu BL (2008) Walking into “Green Rice City of China”—the record of developing modernization of agriculture in Jiansanjiang Administration of Heilongjiang agricultural reclamation. China County Times: 11–13 (011)
Yang JY, Li XP (2009) Application of Wavelet Transform in Well Test Data Processing. Drill Prod Technol 32:42–44
Yildiz A, Akin M, Poyraz M (2009) Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst Appl 36(4):7390–7399
Zhang J, Li G, Liang S (2012) The response of river discharge to climate fluctuations in the source region of the Yellow River[J]. Environ Earth Sci 66(5):1505–1512
Zhao Q (2009) Research on the trend of underground water change in Jiansanjiang area based on gray prediction. J Water Resour Water Eng 20:13–128
Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 41071053, No. 51179032, No. 51279031), new century Talent Supporting Project by Education Ministry, sub-task of National Science and Technology Support Program for Rural Development in the 12th five-year plan of China (No. 2013BAD20B04-S3), special fund of China Postdoctoral Science Foundation (No. 201003410), specialized research fund for the Doctoral Program of Higher Education of China (No. 20102325120009), sub-task of specialized research fund for the Public Welfare Industry of the Ministry of Water Resources (No. 201301096-0201), Natural Science Foundation of Heilongjiang Province of China (No. C201026), specialized research fund for Innovative Talents of Harbin (Excellent Academic Leader) (No. 2013RFXXJ001), postdoctoral scientific research start-up fund of Heilongjiang Province of China (No. LBH-Q11154), Science and Technology Research Program of Education Department of Heilongjiang Province (No. 12531012), Science and Technology Program of Water Conservancy of Heilongjiang Province (No. 201319).
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Liu, D., Fu, Q., Hu, Y. et al. Complexity measure of regional groundwater resources system based on wavelet entropy: a case study of Jiansanjiang Administration of Heilongjiang land reclamation in China. Environ Earth Sci 73, 1033–1043 (2015). https://doi.org/10.1007/s12665-014-3470-8
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DOI: https://doi.org/10.1007/s12665-014-3470-8