Advertisement

Investigation of Multimodel Ensemble Performance Using Machine Learning Method for Operational Dam Safety

  • Hidayah Basri
  • Mohammad Marufuzzaman
  • Lariyah Mohd SidekEmail author
  • Norlela Ismail
Conference paper
Part of the Water Resources Development and Management book series (WRDM)

Abstract

The efficient and effective management of hydropower reservoirs is vital for hydroelectric power plant operation. Therefore, accurate and reliable flow forecasting forms an important basis for efficient real-time hydropower reservoir operation. The inflow forecast modeling process involves various computations and modeling techniques, which results in uncertainties various factors. The inflow forecasting techniques also may vary with the purpose of the system, physical characteristics and availability of data. Over the decades, there is an increasing awareness of the risk of relying on a single model among researchers and practitioners. There is clearly a potential danger in relying entirely on one rainfall-runoff model in such systems, as each model, provides, through its forecast, an important source of information that may be different in some detail from those of the other models calibrated with the same data set. Moreover, the failure of the model to yield consistent and reasonably accurate forecasts may undermine its credibility and results in poor reservoir planning and operation. Hence, consideration of the development of more flexible inflow forecasting systems is needed. These will not be based on a single substantive rainfall-runoff model, but efficient utilization of the synchronous flow forecasts from several substantive rainfall-runoff models, each having different strengths and weaknesses, to produce improved forecasts. In this study, multi-model ensemble strategies will be developed by considering outputs from a committee of models. Multi-model ensemble methods using a machine-learning algorithm combining different model outputs to the water level forecasts will be applied. The performance of the different machine-learning algorithms against the component models and accuracy improvement will be investigated.

Keywords

Machine learning GR4H IFAS PDM Ensemble 

Notes

Acknowledgement

The author would like to thank Universiti Tenaga Nasional for sponsoring this research work under BOLD 2025 Grant.

References

  1. 1.
    Hafiz, I., Nor, N.D.M., Sidek, L.M., Basri, H., Hanapi, M.N., Livia, L.: Application of integrated flood analysis system (IFAS) for Dungun river basin. In: IOP Conference Series: Earth and Environmental Science 16(1):012128 (2013)CrossRefGoogle Scholar
  2. 2.
    Razali, J., Sidek, L.M., Rashid, M.A., Hussein, A., Marufuzzaman, M.: Probable Maximum Precipitation comparison using Hershfield’s statistical method and hydro-meteorological method for Sungai Perak Hydroelectric Scheme. Int. J. Eng. Technol. (UAE) 7(4), 603–608 (2018)Google Scholar
  3. 3.
    Che Ros, F., Tosaka, H., Sidek, L.M., Basri, H.: Homogeneity and trends in long-term rainfall data, Kelantan River Basin, Malaysia. Int. J. River Basin Manag. 14(2), 151–163 (2016)CrossRefGoogle Scholar
  4. 4.
    Hossain, M.S., Sidek, L.M., Marufuzzaman, M., Zawawi, M.H.: Passive congregation theory for particle swarm optimization (PSO): an application in reservoir system operation. Int. J. Eng. Technol. (UAE) (2018)Google Scholar
  5. 5.
    Bai, Y., Chen, Z., Xie, J., Li, C.: Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J. Hydrol. 532(193–206) (2016)CrossRefGoogle Scholar
  6. 6.
    Sammen, S.S., Mohamed, T.A., Ghazali, A.H., Sidek, L.M., El-Shafie, A.: An evaluation of existent methods for estimation of embankment dam breach parameters. Nat. Hazards 87(1), 545–566 (2017)CrossRefGoogle Scholar
  7. 7.
    Madsen, H., Richaud, B., Pedersen, C.B.: A real-time inflow forecasting and reservoir optimization system for optimizing hydropower production.Waterpower XVI, 1–12 (2009)Google Scholar
  8. 8.
    Sidek, L., Basri, H., Lee, L.K., Foo, K.Y.: The Performance of Gross Pollutant Trap for Water Quality Preservation: a Real Practical Application at the Klang Valley. Desalination and Water Treatment, Malaysia (2016)Google Scholar
  9. 9.
    Bourdin, D.R., Fleming, S.W. and Stull, R.B.: Streamflow modelling: a primer on applications, approaches and challenges. Atmosphere-Ocean (2012)Google Scholar
  10. 10.
    Chu, Q., Xu, Z., Chen, Y., Han, D.: Evaluation of the ability of the Weather Research and Forecasting model to reproduce a sub-daily extreme rainfall event in Beijing, China using different domain configurations and spin-up times. Hydrol. Earth Syst. Sci. 22(6), 3391–3407 (2018)CrossRefGoogle Scholar
  11. 11.
    Devia, G.K., Ganasri, B.P., Dwarakish, G.S.: A review on hydrological models. Aquatic Procedia 4, 1001–1007 (2015)CrossRefGoogle Scholar
  12. 12.
    Haris, H., Chow, M.F., Sidek, L.M.: Spatial variability of rainfall in urban catchment. In: Global Civil Engineering Conference (pp. 1075–1086). Springer, Singapore (2017)Google Scholar
  13. 13.
    Duan, Q., Ajami, N.K., Gao, X., Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv. Water Resour. 30(5), 1371–1386 (2007)CrossRefGoogle Scholar
  14. 14.
    Shamseldin, A.Y., O’Connor, K.M., Liang, G.C.: Methods for combining the outputs of different rainfall–runoff models. J. Hydrol. 197(1–4), 203–229 (1997)CrossRefGoogle Scholar
  15. 15.
    Ajami, N.K., Duan, Q., Gao, X., Sorooshian, S.: Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results. J. Hydrometeorol. 7(4), 755–768 (2006)CrossRefGoogle Scholar
  16. 16.
    Shortridge, J.E., Guikema, S.D., Zaitchik, B.F.: Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrol. Earth Syst. Sci. 20(7), 2611–2628 (2016)CrossRefGoogle Scholar
  17. 17.
    Sidek, L.M., Al-Ani, I.A.R., DESA, M.N.M., Basri, N.E.A.: Knowledge-based expert system for Stormwater management in Malaysia. J. Environ. Sci. Technol. 5(5), 381–388 (2014)Google Scholar
  18. 18.
    Marufuzzaman, M., Reaz, M.B.I.: Hardware simulation of pattern matching and reinforcement learning to predict the user next action of smart home device usage. World Appl. Sci. J. 22(9), 1302–1309 (2013)Google Scholar
  19. 19.
    Marufuzzaman, M., Bin Ibne Reaz, M., Rahman, L.F., Farayez, A.: A location based sequence prediction algorithm for determining next activity in smart home. J. Eng. Sci. Technol. Rev. 10(2), 161–165 (2017)CrossRefGoogle Scholar
  20. 20.
    Marufuzzaman, M., Reaz, M.B.I., Ali, M.A.M., Rahman, L.F.: A time series based sequence prediction algorithm to detect activities of daily living in smart home. Methods Inf. Med. (2015)Google Scholar
  21. 21.
    Yaseen, Z.M., El-Shafie, A., Jaafar, O., Afan, H.A., Sayl, K.N.: Artificial intelligence based models for stream-flow forecasting: 2000–2015. J. Hydrol. 530, 829–844 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hidayah Basri
    • 1
  • Mohammad Marufuzzaman
    • 2
  • Lariyah Mohd Sidek
    • 1
    • 2
    Email author
  • Norlela Ismail
    • 1
  1. 1.Civil Engineering Department, College of EngineeringUniversiti Tenaga NasionalSelangorMalaysia
  2. 2.Institute of Energy InfrastructureUniversiti Tenaga NasionalSelangorMalaysia

Personalised recommendations