Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and Artificial Fish Swarm Algorithm
- 339 Downloads
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.
KeywordsTime series fractal dimension Artificial fish swarm algorithm Groundwater depth Complexity Jiansanjiang Branch Bureau
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.
- Belward JA (1999) Fractal dimensions for rainfall time series. Math Comput Simul 48(4–6):437–446Google Scholar
- Li XL, Qian JX (2003) Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques. Chin J Semicond 8(1):1–6Google Scholar
- Liu D, Yu M, Sun N, et al. (2011) Study on dynamic variation regularities of regional groundwater depth based on complexity diagnosis. Proceeding of The 5th International Conference on Computer and Computing Technologies in Agriculture, PartII:72–83Google Scholar
- Ni LP (2010) Research on financial data analytical method based on fractal technology. Hefei University of Technology, HefeiGoogle Scholar
- Qu LD, He DX (2008) Bi-group artificial fish-school algorithm based on simplex method. J Comput Appl 28(8):2103–2104Google Scholar
- Shi QH, Wang LG (2010) Simulated formula between water level and water flux based on a simplified artificial fish swarm algorithm. J Gansu Agric Univ 45(2):147–151Google Scholar
- Suleymanov AA, Abbasov AA, Ismaylov AJ (2009a) Application of fractal analysis of time series in oil and gas production. Pet Sci Technol (27):915–922Google Scholar
- Wang XH, Zheng XM, Xiao JM (2007) Artificial Fish-Swarm Algorithm for solving constrained optimization problems. Comput Eng Appl 43(3):41–45Google Scholar
- Wang LG, Hong Y, Zhao FQ et al (2009) Simplified artificial fish swarm algorithm. J Chin Comput Syst 30(8):1663–1667Google Scholar
- Zhang P (2009) Nonlinear study on surrounding rock stability of Baihe Tunnel based on wavelet theory. Jilin University, ChangchunGoogle Scholar