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Prediction Model of Water Resources in Mine Area Based on Phase Space Reconstruction and Chaos Neural Network

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Advances in Computation and Intelligence (ISICA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5370))

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Abstract

In the process of social economic development, water resource is increasingly scarce because of unreasonable exploitation of groundwater resources. It has seriously hampered the economic and social development speed in mine area, and even caused a series of negative effects of serious environmental and ecological problems. In this paper, chaos theory is used to study the water resource system in mine area. By analyzing the phenomena of chaotic characteristics in water resource system, regional mine water resources safety model was constructed based on the phase space reconstruction coupled with the neural network. Through the application of the model to forecast future water resources consumption in Gejiu mine area, the predicted results not only verified the validity of the model, but also found a new approach to study water resources in mine area.

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References

  1. Liu, S.Y., Zhu, S.J., Yu, X.: Determinating the embedding dimension in phase space reconstruction. Journal of Harbin Engineering University 4, 374–381 (2008)

    MATH  Google Scholar 

  2. Xiao, F.H., Yan, G.R., Han, Y.: Information theory approach to determine embedding parameters for phase space reconstruction of chaotic time series. Acta Physica Sinica 2, 550–556 (2005)

    Google Scholar 

  3. Yilmaz, L.: Chaos in the water resources system. Applied Mathematics and Computation 2, 761–773 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Sua, Z.-Y., Wub, T., Yangc, P.-H., Wang, Y.-T.: Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach. Physica A: Statistical Mechanics and its Applications 10, 2293–2305 (2008)

    Article  Google Scholar 

  5. Deng, J., Yue, Z.Q., Tham, L.G., Zhu, H.H.: Pillar design by combining finite element methods, neural networks and reliability: a case study of the Feng Huangshan copper mine, China. International Journal of Rock Mechanics and Mining Sciences 4, 585–599 (2003)

    Article  Google Scholar 

  6. Lo, E.Y., Mei, C.C.: Slow evolution of nonlinear deep water waves in two horizontal directions: A numerical study. Wave Motion 3, 245–259 (1987)

    Article  MATH  Google Scholar 

  7. Urban, L.V., Templer, O.W.: Water resource management on the Texas High Plains: controlled chaos or a model of efficiency? Management of Irrigation and Drainage Systems: Integrated Perspectives, 116–123 (1993)

    Google Scholar 

  8. Yang, X.H., Li, J.Q.: Source: Application of chaos real-encoded genetic algorithm in water quality model parameter optimization. Water Resources and Power 5, 1–4 (2006)

    Google Scholar 

  9. Napiorkowski, J.J., Terlikowski, T.: Application of deterministic chaos and neural networks in water reservoir management. In: AIP Conference Proceedings, vol. 573, pp. 349–359 (2001)

    Google Scholar 

  10. Liu, Y.J., Shen, J.S.: Prediction model of urban water consumption based on chaos neural network. Water Resources and Power 1, 15–27 (2005)

    Google Scholar 

  11. Cao, L.H., Hao, S.L., Chen, N.X.: Study on resource quantity of surface water based on phase space reconstruction and neural network. Journal of Coal Science and Engineering 1, 39–42 (2006)

    Google Scholar 

  12. Sivakumar, B., Jayawardena, A.W., Fernando, T.M.K.G.: River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology 30, 225–245 (2002)

    Article  Google Scholar 

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhou, K., Gao, G., Gao, F., Gao, W. (2008). Prediction Model of Water Resources in Mine Area Based on Phase Space Reconstruction and Chaos Neural Network. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_36

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  • DOI: https://doi.org/10.1007/978-3-540-92137-0_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92136-3

  • Online ISBN: 978-3-540-92137-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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