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Efficiency of a Neuro-Fuzzy Model Based on the Hilbert-Huang Transform for Flood Prediction

  • Zaki AbdaEmail author
  • Mohamed Chettih
  • Bilel Zerouali
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Flooding is a natural phenomenon, which constitutes a threat that could lead to loss of human life and material property. It constitutes the first major risk. During the last years, artificial intelligence has been widely applied in the field of hydrology and in many other fields of hydraulic engineering. The Hilbert-Huang Transform (HHT) is a new signal processing technique in the analysis of non-stationary time series, particularly effective for hydrological series. Currently, the application of intelligent hybrid systems in different areas has shown a good performance and an unequalled efficiency. As such, the hybrid technique of an adaptive neuro-fuzzy inference system (ANFIS) coupled to the Hilbert-Huang transform (HHT-ANFIS), was used in this study to estimate daily flow rates in Algiers’ coastal basin. The results obtained are very encouraging and more efficient than those obtained by the neuro-fuzzy inference model and the classical multiple linear regression (MLR) model.

Keywords

Prediction Flow Intelligent hybrid model Hilbert-Huang transform Neuro-Fuzzy system 

References

  1. 1.
    Kuczera, G., Franks, S.W.: Testing hydrologic models: fortification or falsification? In: Singh, V.P., Frevert, D.K. (eds.) Mathematical Modelling of Large Watershed Hydrology. Water Resources Publications, Littleton (2002)Google Scholar
  2. 2.
    Vrugt, J.A., Diks, C.G.H., Gupta, H.V., Bouten W., Verstraten, J.M.: Improved treatment of uncertainty in hydrological modelling: combining the strengths of global optimization and data assimilation. Water Resour. Res. 41 (2005)Google Scholar
  3. 3.
    Rajurkar, M.P., Kothyari, U.C., Chaube, U.C.: Artificial neural networks for daily rainfall–runoff modelling. Hydrol. Sci. J. 47, 865–877 (2002)CrossRefGoogle Scholar
  4. 4.
    Kisi, O., Shiri, J.: Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour. Manag. 25, 3135–3152 (2011)CrossRefGoogle Scholar
  5. 5.
    Parmar, K.S., Bhardwaj, R.: Water quality management using statistical and time series prediction model. App. Water Sci. 4(4), 425–434 (2014)CrossRefGoogle Scholar
  6. 6.
    Parmar, K.S., Bhardwaj, R.: River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour. Manag. 29(1), 17–33 (2015)CrossRefGoogle Scholar
  7. 7.
    Huang, N.E., Shen, Z., Long, S.R., Wu M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London A: Math., Phys. Eng. Sci. (1998)Google Scholar
  8. 8.
    Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cyber. 23, 665–685 (1993)Google Scholar
  9. 9.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cyber. 1, 116–132 (1985)Google Scholar
  10. 10.
    Sugeno, M., Kang, G.: Fuzzy modelling and control of multilayer incinerator. Fuzzy Sets Syst. 18, 329–345 (1986)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Laboratory of Water Resources, Soil and Environment, Department of Civil EngineeringAmar Telidji UniversityLaghouatAlgeria

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