Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions

详细信息    查看全文

• 作者：Zhenfang He (1) (2) (3)
  Yaonan Zhang (1)
  Qingchun Guo (3) (4)
  Xueru Zhao (1) (5)
  
• 关键词：Artificial neural network ; Wavelet artificial neural network ; Groundwater depth ; Training algorithms ; Mallat decomposition algorithm ; Fractal dimension
• 刊名：Water Resources Management
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：28
• 期：15
• 页码：5297-5317
• 全文大小：2,451 KB
• 参考文献：1. Ahmad A, El-Shafie A et al (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28(11):3391鈥?405 CrossRef
  2. Altunkaynak A (2014) Predicting water level fluctuations in Lake Michigan-Huron using wavelet-expert system methods. Water Resour Manag 28(8):2293鈥?314 CrossRef
  3. Banerjee P, Singh VS, Chatttopadhyay K, Chandra PC, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3鈥?):212鈥?20 CrossRef
  4. Basheer IA, Hajmeer M (2000) Artificial neural networks fundamentals, computing, design, and application. J Microbiol Methods 43:3鈥?1 CrossRef
  5. Breslin MC, JAB (1999) Fractal dimensions for rainfall time series. Mathematics and Computers in Simulation 1999 (48):437鈥?46
  6. Buczkowski PH, Cartilier L (1998) Measurements of fractal dimension by box-counting: a critical analysis of data scatter. Physica A 252(1998):23鈥?4 CrossRef
  7. Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth Parts A/B/C 31(18):1164鈥?171 CrossRef
  8. Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in yangtze river. J Hydrol Eng 2005(10):485鈥?91 CrossRef
  9. Chen W, Chau KW (2006) Intelligent manipulation and calibration of parameters for hydrological models. Int J Environ Pollut 28(3鈥?):432鈥?47 CrossRef
  10. Chen C-W, Wei C-C et al (2014) Application of neural networks and optimization model in conjunctive use of surface water and groundwater. Water Resour Manag 28(10):2813鈥?832 CrossRef
  11. Cheng CT, Chau KW, Sun JYG, Lin Y (2005) Long-term prediction of discharges in Manwan hydropower using adaptive-network-based fuzzy inference systems models. Lecture Notes in Computer Science. 2005(3612): 1152-1161
  12. Chou CM (2007) Efficient nonlinear modeling of rainfall-runoff process using wavelet compression. J Hydrol 332(3鈥?):442鈥?55 CrossRef
  13. Coppola E, Szidarovszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. J Hydrol Eng 8(6):348鈥?60 CrossRef
  14. Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885鈥?96 CrossRef
  15. Dariane A, Karami F (2014) Deriving hedging rules of multi-reservoir system by online evolving neural networks. Water Resour Manag 28(11):3651鈥?665 CrossRef
  16. Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional ground water levels affected by human activities. Ground Water 46(1):80鈥?0
  17. Foroutan-pour K, Dutilleul P, Smith DL (1999) Advances in the implementation of the box-counting method of fractal dimension estimation. Appl Math Comput 105:195鈥?10 CrossRef
  18. Gaur S, Ch S, Graillot D, Chahar BR, Kumar DN (2012) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manag 27(3):927鈥?41 CrossRef
  19. Goyal M (2014) Modeling of sediment yield prediction using M5 model tree algorithm and wavelet regression. Water Resour Manag 28(7):1991鈥?003 CrossRef
  20. Kim S, Shiri J, Kisi O, Singh VP (2013) Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour Manag 27(7):2267鈥?286 CrossRef
  21. Kumar ARS, Goyal MK, Ojha CSP, Singh RD, Swamee PK, Nema RK (2012) Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules. Water Resour Manag 27(3):911鈥?25 CrossRef
  22. Latt Z, Wittenberg H (2014) Improving flood forecasting in a developing country: a comparative study of stepwise multiple linear regression and artificial neural network. Water Resour Manag 28(8):2109鈥?128 CrossRef
  23. Li, XY, Chau KW, et al (2006) A Web-based flood forecasting system for Shuangpai region Advances in Engineering Software 37(3): 146-158
  24. Liu HH, Zhang R, Bodvarsson GS (2005) An active region model for capturing fractal flow patterns in unsaturated soils: model development. J Contam Hydrol 80:18鈥?0 CrossRef
  25. Maciej R, Kundzewicz ZW (1997) Fractal analysis of flow of the river Warta. J Hydrol 200:280鈥?94 CrossRef
  26. Mallat SG (1989) Multifrequency channel decompositions of images and wavelet models. Acoust Speech Signal Process IEEE Trans 37(12):2091鈥?110 CrossRef
  27. Mandelbrot BB (1982) The Fractal Geometry of Nature. W.H. Freeman, New York, NY, USA
  28. Mandelbrot BB (1989) Fractal geometry: what is it, and what does it do? In: Tildesley D, Ball RC (eds) FRS Fleischmann. Fractals in the Natural Sciences Princeton University Press, Princeton, NJ
  29. Mandelbrot BB, Dann E, Passoja J, Paulay A (1984) Fractal character of fracture surfaces of metals. Nature 308(19):721鈥?22 CrossRef
  30. Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a River Island of Eastern India. Water Resour Manag 24(9):1845鈥?865 CrossRef
  31. Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27(5):1301鈥?321 CrossRef
  32. Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20(1):77鈥?0 CrossRef
  33. Rene ER, Estefania Lopez M, Veiga MC, Kennes C (2011) Neural network models for biological waste-gas treatment systems. New Biotechnol 29(1):56鈥?3 CrossRef
  34. Sahoo GB, Ray C, Mehnert E, Keefer DA (2006) Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Sci Total Environ 367(1):234鈥?51 CrossRef
  35. Sang Y-F (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8鈥?5 CrossRef
  36. Satyaji Rao YR, Krishna B, Nayak PC (2011) Time series modeling in water resources planning and management. Int J Earth Sci Eng 4(6):247鈥?53
  37. Schuller DJ, Rao AR, Jeong GD (2001) Fractal characteristics of dense stream networks. J Hydrol 2001(243):1鈥?6 CrossRef
  38. Seckin N, Cobaner M, Yurtal R, Haktanir T (2013) Comparison of artificial neural network methods with L-moments for estimating flood flow at ungauged sites: the case of East Mediterranean River Basin, Turkey. Water Resour Manag 27(7):2103鈥?124 CrossRef
  39. Sehgal V, Sahay R et al (2014) Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resour Manag 28(6):1733鈥?749 CrossRef
  40. Takayasu H (1990) Fractals in the physical sciences. Manchester University Press, Manchester
  41. Umut O (2012) Using wavelet transform to improve generalization capability of feed forward neural networks in monthly runoff prediction. Scientific Research and Essays 7 (17)
  42. Wu CL, Chau KW et al (2009) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45(8):1鈥?3 CrossRef
  43. Xu Y (2005) Explanation of scaling phenomenon based on fractal fragmentation. Mech Res Commun 32(2):209鈥?20 CrossRef
  44. Xu J, Chen Y et al (2014) Integrating wavelet analysis and BPANN to simulate the annual runoff with regional climate change: a case study of Yarkand River, Northwest China. Water Resour Manag 28(9):2523鈥?537 CrossRef
  
• 作者单位：Zhenfang He (1) (2) (3)
  Yaonan Zhang (1)
  Qingchun Guo (3) (4)
  Xueru Zhao (1) (5)
  
  1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China
  2. Liaocheng University, Liaocheng, 252059, China
  3. University of Chinese Academy of Sciences, Beijing, 100049, China
  4. Shaanxi Radio & TV University, Xi鈥檃n, 710068, China
  5. Lanzhou University, Lanzhou, 730000, China
  
• ISSN：1573-1650

文摘

The objective of this study was comparative study of artificial neural networks (ANN) and wavelet artificial neural networks (WANN) for time-series groundwater depth data (GWD) forecasting with various curve fractal dimensions. The paper offered a better method of revealing the change characteristics of GWD. Time series prediction based on ANN algorithms is fundamentally difficult to capture the data change details, when the time-series GWD data changes are more complex. For this purpose, Wavelet analysis and fractal theory methods are proposed to link to ANN models in predicting GWD and analysis the change characteristics. The trend and random components were separated from the original time-series GWD using wavelet methods. The fractal dimension is convenient for quantitatively describing the irregularity or randomness of time series data. Three types of training algorithms for ANN and WANN models using a Mallat decomposition algorithm were investigated as case study at three sites in the Ganzhou region of northwest China to find an optimal model that is suitable for certain characteristics of time-series GWD data. The simulation results indicate that both WANN and ANN models with the Bayesian regularization algorithm are accurate in reproducing GWD at sites with smaller fractal dimensions. However, WANN models alone are suitable for sites at which the fractal dimension of the wavelet decomposition detail components is larger. Prediction error is also greater when the fractal dimension is larger.