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Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models


Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

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This work was supported by a research grant coded TRGS/1/2015/UKM/02/5/1 Universiti Kebangsaan Malaysia. The authors would like to thank so much the Ministry of Higher Education, FRGS/1/2016/STG06/UKM/02/1 and the University of Malaya Research Grant (UMRG) coded RP025A-18SUS.

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Correspondence to Mohammed Falah Allawi.

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Responsible editor: Philippe Garrigues

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Allawi, M.F., Jaafar, O., Mohamad Hamzah, F. et al. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environ Sci Pollut Res 25, 13446–13469 (2018).

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  • Hydrological parameters
  • Predictive models
  • Optimization models
  • Environmental