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Evaluation of Several Algorithms in Forecasting Flood

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

Abstract

Precise flood forecasting is desirable so as to have more lead time for taking appropriate prevention measures as well as evacuation actions. Although conceptual prediction models have apparent advantages in assisting physical understandings of the hydrological process, the spatial and temporal variability of characteristics of watershed and the number of variables involved in the modeling of the physical processes render them difficult to be manipulated other than by specialists. In this study, two hybrid models, namely, based on genetic algorithm-based artificial neural network and adaptive-network-based fuzzy inference system algorithms, are employed for flood forecasting in a channel reach of the Yangtze River. The new contributions made by this paper are the application of these two algorithms on flood forecasting problems in real prototype cases and the comparison of their performances with a benchmarking linear regression model in this field. It is found that these hybrid algorithms with a “black-box” approach are worthy tools since they not only explore a new solution approach but also demonstrate good accuracy performance.

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References

  1. 1.
    Chau, K.W., Jiang, Y.W.: 3D Numerical Model for Pearl River Estuary. Journal of Hydraulic Engineering ASCE 127(1), 72–82 (2001)CrossRefGoogle Scholar
  2. 2.
    Chau, K.W., Jin, H.S.: Numerical Solution of Two-Layer, Two-Dimensional Tidal Flow in a Boundary Fitted Orthogonal Curvilinear Coordinate System. International Journal for Numerical Methods in Fluids 21(11), 1087–1107 (1995)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chau, K.W., Jin, H.S., Sin, Y.S.: A Finite Difference Model of Two-Dimensional Tidal Flow in Tolo Harbor, Hong Kong. Applied Mathematical Modelling 20(4), 321–328 (1996)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chau, K.W., Lee, J.H.W.: Mathematical Modelling of Shing Mun River Network. Advances in Water Resources 14(3), 101–124 (1991)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Chau, K.W., Lee, J.H.W.: A Microcomputer Model for Flood Prediction with Application. Microcomputers in Civil Engineering 6(2), 109–121 (1991)CrossRefGoogle Scholar
  6. 6.
    Smith, J., Eli, R.N.: Neural-Network Models of Rainfall-Runoff Process. Journal of Water Resources Planning and Management, ASCE 121(6), 499–508 (1995)CrossRefGoogle Scholar
  7. 7.
    Tokar, A.S., Johnson, P.A.: Rainfall-Runoff Modeling using Artificial Neural Networks. Journal of Hydrologic Engineering, ASCE 4(3), 232–239 (1999)CrossRefGoogle Scholar
  8. 8.
    Liong, S.Y., Lim, W.H., Paudyal, G.N.: River Stage Forecasting in Bangladesh: Neural Network Approach. Journal of Computing in Civil Engineering, ASCE 14(1), 1–8 (2000)CrossRefGoogle Scholar
  9. 9.
    Cheng, C.T., Chau, K.W.: Fuzzy Iteration Methodology for Reservoir Flood Control Operation. Journal of the American Water Resources Association 37(5), 1381–1388 (2001)CrossRefGoogle Scholar
  10. 10.
    Chau, K.W., Cheng, C.T.: Real-time Prediction of Water Stage with Artificial Neural Network Approach. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS, vol. 2557, pp. 715–715. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Chau, K.W.: Calibration of Flow and Water Quality Modeling using Genetic Algorithm. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS, vol. 2557, p. 720. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Cheng, C.T., Ou, C.P., Chau, K.W.: Combining a Fuzzy Optimal Model with a Genetic Algorithm to solve Multiobjective Rainfall-Runoff Model Calibration. Journal of Hydrology 268(1-4), 72–86 (2002)CrossRefGoogle Scholar
  13. 13.
    Chau, K.W.: River Stage Forecasting with Particle Swarm Optimization. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS, vol. 3029, pp. 1166–1173. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Chau, K.W.: Rainfall-Runoff Correlation with Particle Swarm Optimization Algorithm. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 970–975. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Cheng, C.T., Chau, K.W., Sun, Y.G., Lin, J.Y.: Long-Term Prediction of Discharges in Manwan Reservoir using Artificial Neural Network Models. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 1040–1045. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Goldberg, D.E., Kuo, C.H.: Genetic Algorithms in Pipeline Optimization. Journal of Computing in Civil Engineering ASCE 1(2), 128–141 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Department of Civil and Structural EngineeringHong Kong Polytechnic UniversityKowloon, Hong KongPeople’s Republic of China

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