Journal of Software, Vol 6, No 4 (2011), 716-723, Apr 2011
doi:10.4304/jsw.6.4.716-723

Integration of Grey with Neural Network Model and Its Application in Data Mining

Changjun Zhu, Qinghua Luan, Zhenchun Hao, Qin ju

Abstract


Because of Boundary types and geologic conditions, which possess random and obscure characteristics, groundwater heads vary with the conditions. The prediction of groundwater level is one of the main work of hydraulic government, which is predicted based on the history data and the relative influence factors. Therefore, prediction precision depends on the accuracy of history data. Data mining has provided a new method for analyzing massive, complex and noisy data. According to the complexity and ambiguity of groundwater system, a new integration of grey with neural network model is built to forecast groundwater heads, which were used to judge whether future groundwater heads were extraordinarily over the history range or not.  This method overcomes the disadvantages which the grey method only predict the linear trend. The methods were used to analyze the random characteristics of groundwater heads in anyang city. The results indicate that the method is reliable, and reasonable


Keywords


grey degree;groundwater level;neural network; Anyang city

References


[1] Martin T.Hagan, Howard B.Demuth, Mark H.Beale, Neural network design.Beijing:China Machine Press,2004,239-255(in Chinese)

[2] Linming Zhao, Haoyun Hu, Dehua Wei, Shuqian Wang. Multilayer forward artificial neural network. Zhengzhou:Yellow River Conservancy Press,1999,24-35(in Chinese)

[3] Bao Liu, Daiping Hu. Studies on applying artificial neural networks to some forecasting problems. Journal of System Engineering.1999,338-343(in Chinese)

[4] Zhijia Li, Xiangguang Kong. Channel flood routing model of artificial neural network. Journal of Hohai University. 1997,25(5):7-12(in Chinese)

[5] Li, R., Wang, J., Qian, J., 2002. Prediction of river water quality based on grey dynamic model group. Bulletin of Soil and Water Conservation 22(4), 10-12(in Chinese)

[6] Xiaofang Rui, Linli Wang. A study of flood routing method with forecast period. Advances in Water Science,2000,11(3):291-295.(in Chinese)

[7] Guoru Huang, Xiaofang Rui. Radial basis function-neural network model for channel flood routing. Journal of Hohai University (Natural Sciences), 2003,31(6):621-625(in Chinese)

[8] Xingming Zhu, Changna Lu, Ruyun Wang, Jingyi Bai, Artificial neural network model for flood water level forecasting . Journal of Hydraulic Engineering, 2005,36(7):806-811(in Chinese)

[9] Yanfang Geng, Zhili Wang, Sheng Jin. River system flood forecasting based on artificial neural network of radial basis function. Journal of Dalian University of Technology, 2006,4(2):267-271(in Chinese)

[10] Feisi Research and Development Center of Science and Technology, MATAB6.5 auxiliary neural network analysis and design.Beijing, electron industry press, 2003(in Chinese)

[11] Luo, D., Guo, Q., Wang, X., 2003. Simulation and prediction of underground water dynamics based on RBF neural network. Acta Geoscientia Sinica 24(5), 475-478(in Chinese)

[12] Jun Lang,Xiaohong Su,Xiujie Zhou. Prediction for air pollution index based on combined grey neural network model.Journal of Harbin institute of techonology,2004,36(12):1598(in Chinese)

[13] Y.-L.Yeh, T.-C.Chen. Grey degree and grey prediction of groundwater head. Stoch Enviro Risk Ass,2004,18:351-363.
doi:10.1007/s00477-004-0184-6

[14] Qin Ju, Zhongbo Yu, Zhenchun Hao, Xi Chen, Chao She. Integration of artificial neural networks with a numerical groundwater model for simulating spring discharge [A].2010 4th International Conference on Intelligent Information Technoogy Application[C]. Institute of Electrical and Electronics Engineers, Vol.2, p361-364.

[15] Yeh YL, Su MD, Tsou I (1995). Forecasting of groundwater level using grey modeling. J Taiwan Water Conservancy 43: 66–73 (in Chinese)

[16] Hogg RV, Tanis EA (1988) Probability and statistical inference. Macmillan Publishing Company, New York

[17] CHEN Xi, LIU Chuan-jie, HU Zhong-ming, et al. Numerical modeling of groundwater in a spring catchment and prediction of variations in the spring discharge[J]. Hydrogeology and Engineering Geology, 2006, 2:36-40.

[18] Ju, Q., Z. Yu, Z. Hao, et al, 2009, Division-based Rainfall-Runoff Simulations with BP Neural Networks and Xinanjiang models [J]. Neurocomputing, 72, 2873-2883.
doi:10.1016/j.neucom.2008.12.032

[19] Parkin, G., S. J., Birkinshaw, P. L., Younger, et al, 2007, A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows, Journal of Hydrology, 339, 15-28.
doi:10.1016/j.jhydrol.2007.01.041

[20] Birkinshaw, S. J., G. Parkin and Z. Rao, 2008, A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts, Journal of Hydroinformatics, 10(2):127-137.
doi:10.2166/hydro.2008.014

[21] Demissie, Y., K., A. J. Valocchi, B. S. Minsker, et al, Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions, Journal of Hydrology, 364, 257–271.
doi:10.1016/j.jhydrol.2008.11.007

[22] Garcia, L., A., A. Shigidi, 2006, Using neural networks for parameter estimation in ground water, Journal of Hydrology, 318, 215–231.
doi:10.1016/j.jhydrol.2005.05.028

[23] McDonaldM, C., and A.W. Harbaugh, 1988. A modular three-dimensional finite difference ground-water flow model: U.S. Geological Survey Techniques of Water Resources Investigations[R]. Book 6, Chapter A1, 586.

[24] Douglas P. Dufresne, and Charles W. Drake, 1999. Regional groundwater flow model construction and wellfield site selection in a karst area, Lake City, Florida [J]. Engineering Geology, 52(1):129-139.
doi:10.1016/S0013-7952(98)00066-0

[25] Chen X., and X. Chen, 2003. Stream Water Infiltration, Bank Storage, and Storage Zone due to Flood Storages in Channels[J]. Journal of Hydrology, 280, 246-264.
doi:10.1016/S0022-1694(03)00232-4

[26] CHEN Xi, CHEN Xun-hong. Numerical modeling of groundwater flow and analysis of water budget in Nebraska Sand Hills, USA[J]. Advances in Water Science, 2004, 15:94 -99.

[27]Qin Ju, Zhongbo Yu, Zhenchun Hao, et al, 2007. Hydrologic Simulations with Artificial Neural Networks [A]. Angela Burgess. Third International Conference on Natural Computation [C]. Los Alamitos: IEEE Computer Society Publications, 2:22-27


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