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Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

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Abstract

The accurate prediction of hourly runoff discharge in a river basin during typhoon events is of critical importance in operational flood control and management. This study utilizes three model approaches to predict runoff discharge in the Laonong Creek basin in southern Taiwan: the hydrological engineering center hydrological modeling system (HEC-HMS) model and two hybrid models which combine the HEC-HMS model with a genetic algorithm neural network (GANN) and an adaptive neuro-fuzzy inference system approach (ANFIS). Hourly runoff discharge data during seven heavy rainfall/typhoon events were collected for model calibration (training) and validation. Six statistical indicators [i.e., mean absolute error, root-mean-square error, coefficient of correlation, error of time to peak discharge, error of peak discharge, and coefficient of efficiency (CE)] were used to evaluate the prediction accuracy. The simulation results indicate that the HEC-HMS model cannot satisfactorily predict hourly runoff discharge during the typhoon events. Both hybrid approaches that use the HEC-HMS model in conjunction with the GANN and ANFIS models can significantly improve the prediction accuracy for the n-h-ahead runoff discharge.

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References

  1. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1(1):67–71

    MathSciNet  Google Scholar 

  2. Arabi M, Frankenberger JR, Engel BA, Arnold JG (2008) Representation of agricultural practices with SWAT. Hydrol Process 22(16):3042–3055

    Article  Google Scholar 

  3. Pektas AO, Cigizoglu HK (2013) ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J Hydrol 500:21–36

    Article  Google Scholar 

  4. Joo J, Kjeldsen T, Kim HJ (2014) A comparison of two event-based flood models (ReFH-rainfall runoff model and HEC-HMS) at two Korean catchments, Bukil and Jeungpyeong). KSCE J Civ Eng 18(1):330–343

    Article  Google Scholar 

  5. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80–108

    Article  Google Scholar 

  6. Abrahart RJ, Kneale PE, See L (2004) Neural networks for hydrological modelling. Taylor & Francis, New York, p 304

    Google Scholar 

  7. Tabari H, Marifi S, Abyaneh HZ, Sharifi MR (2010) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput Appl 19(4):625–635

    Article  Google Scholar 

  8. Talei A, Chua LHC, Quek C, Jansson PE (2013) Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. J Hydrol 488:17–32

    Article  Google Scholar 

  9. Machado F, Mine M, Kaviski E, Fill H (2011) Monthly rainfall–runoff modelling using artificial neural networks. Hydrol Sci J 56(3):349–361

    Article  Google Scholar 

  10. Nourani V, Komasi M, Alami MT (2012) Hybrid wavelet-genetic programming approach to optimize ANN modeling of rainfall–runoff process. J Hydrol Eng ASCE 17(6):724–741

    Article  Google Scholar 

  11. Rezaeianzadeh M, Tabari H, Yazdi AA, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25(1):25–37

    Article  Google Scholar 

  12. Rezaeianzadeh M, Stein A, Tabari H, Abghari H, Jalalkamali N, Hosseinipour EZ, Singh VP (2013) Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. Int J Environ Sci Technol 10(6):1181–1192

    Article  Google Scholar 

  13. Shamseldin AY, O’Connor KM (2001) A non-linear neural network technique for updating of river flow forecasts. Hydrol Earth Syst Sci 5(4):577–597

    Article  Google Scholar 

  14. Xiong L, O’Connor KM (2002) Comparison of four updating models for real-time river flow forecasting. Hydrol Sci J 47(4):621–639

    Article  Google Scholar 

  15. Shamseldin AY (1997) Application of a neural network technique to rainfall–runoff modelling. J Hydrol 199(3–4):272–294

    Article  Google Scholar 

  16. See L, Abrahart RJ (2001) Multi-model data fusion for hydrological forecasting. Comput Geosci 27(8):987–994

    Article  Google Scholar 

  17. Ajami NK, Duan Q, Gao X, Sorooshian S (2006) Multimodel combination techniques for analysis of hydrological simulations: application to distributed model intercomparison project results. J Hydrometeorol 7(4):755–768

    Article  Google Scholar 

  18. Shamseldin AY, O’Connor KM, Nasr AE (2007) A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall–runoff models. Hydrol Sci J 52(5):896–916

    Article  Google Scholar 

  19. Viney NR, Bormann H, Breuer L, Croke BFW, Frede H, Graff T, Hubrechts L, Huisman JA, Jakeman AJ, Kite GW, Lanini J, Leavesley G, Lettenmaier DP, Lindstrom G, Seibert J, Sivapalan M, Willems P (2009) Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). II: ensemble combinations and predictions. Adv Water Resour 32(2):147–158

    Article  Google Scholar 

  20. Fernando AK, Shamseldin AY, Abrahart RJ (2012) Use of gene expression programming for multimodel combination of rainfall–runoff models. J Hydrol Eng 17(9):975–985

    Article  Google Scholar 

  21. Phukoetphim P, Shamseldin AY, Melville BW (2014) Knowledge extraction from artificial neural networks for rainfall–runoff model combination system. J Hydrol Eng 19(7):1422–1429

    Article  Google Scholar 

  22. Panda RK, Pramanik N, Bala B (2010) Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput Geosci 36(6):735–745

    Article  Google Scholar 

  23. Napolitano G, See L, Calvo B, Savi F, Heppenstall A (2010) A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome. Phys Chem Earth 35(3–5):187–194

    Article  Google Scholar 

  24. Talei A, Chua LHC, Wang TSW (2010) Evaluation of rainfall and discharge inputs used by adaptive network-based fuzzy inference systems (ANFIS) in rainfall–runoff modeling. J Hydrol 391(3–4):248–262

    Article  Google Scholar 

  25. Young CC, Liu WC (2015) Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model. Hydrol Sci J (in press)

  26. Feldman AD (2000) Hydrologic modeling system HEC-HMS. Technical reference manual. U.S. Army Corps of Engineers, Davis

  27. US Army Corps Engineers (2008) Hydrologic modeling system (HEC-HMS) application guide: version 3.1.0. Institute for Water Resources, Davis

  28. US Soil Conservation Service (1986) Urban hydrology for small watershed. Technical release 55. US Department of Agriculture

  29. McCuen RH (1998) Hydrologic analysis and design. Prentice-Hall, Upper Saddle River, pp 155–163

    Google Scholar 

  30. Clark CO (1945) Storage and the unite hydrograph. Trans Am Soc Civil Eng 110:1419–1488

    Google Scholar 

  31. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New York

    Google Scholar 

  32. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Longman, New York

    MATH  Google Scholar 

  33. Karimi H, Yousefi F (2012) Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids. Fluid Phase Equilib 336:79–83

    Article  Google Scholar 

  34. Ferentinos KP (2005) Biological engineering applications of feed forward neural networks designed and parameterized by genetic algorithms. Neural Netw 18(7):934–950

    Article  Google Scholar 

  35. Karzynski M, Mateos A, Herrero J, Dopazo J (2003) Using a genetic algorithm and a perceptron for feature selection and supervised class learning in DNA microarray data. Artif Intell Rev 20(1–2):39–51

    Article  Google Scholar 

  36. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  37. Nayak PC, Sudheer KP, Ragan DM, Ramasastri KS (2004) A neuro fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–20):52–66

    Article  Google Scholar 

  38. MATLAB (2008) Fuzzy logic toolbox-user guide. The MathWorks, MA, USA

  39. Young CC, Wu CH (2009) An efficient and accurate non-hydrostatic model with embedded Boussinesq-type like equations for surface wave modeling. Int J Numer Meth Fluids 60(1):27–53

    Article  MATH  Google Scholar 

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Acknowledgments

This research was conducted with the support of the National Science Council Grant No. 102-2625-M-239-002. This financial support is greatly appreciated. The authors would like to express their appreciation to the Water Resources Agency for providing access to their recorded data.

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Authors and Affiliations

  1. Hydrotech Research Institute, National Taiwan University, Taipei, 10617, Taiwan

    Chih-Chieh Young

  2. Department of Civil and Disaster Prevention Engineering, National United University, Miao-Li, 36063, Taiwan

    Wen-Cheng Liu & Chung-En Chung

  3. Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei, 10093, Taiwan

    Wen-Cheng Liu

Authors
  1. Chih-Chieh Young
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  2. Wen-Cheng Liu
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  3. Chung-En Chung
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Correspondence to Wen-Cheng Liu.

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Young, CC., Liu, WC. & Chung, CE. Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge. Neural Comput & Applic 26, 1631–1643 (2015). https://doi.org/10.1007/s00521-015-1832-0

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  • DOI: https://doi.org/10.1007/s00521-015-1832-0

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