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Development of the marine predators algorithm for optimizing the performance of water supply reservoirs

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Abstract

Optimal reservoir operation involves complex decision making. The marine predators algorithm (MPA) is herein applied and evaluated with several mathematical functions and with the optimized operation of the Aydogmush reservoir (East Azerbaijan province, Iran) that minimizes the deficit of agricultural water supplied to downstream lands. Reservoir operation covers a baseline period (1987–2000) and a period of climate change (2026–2039). The MPA’s reservoir operation results are compared those obtained with the genetic algorithm (GA). The calculated operating policies are evaluated based on indexes of reliability, resiliency and vulnerability. A comparison of reservoir water releases, water supply deficit and reservoir storage from five runs under baseline and climate change periods indicates better performance of the MPA-calculated reservoir operation than the GAs in meeting downstream water demand. The efficiency indexes show that, for example, the reliability of the operating policy obtained with the MPA is larger by 54 and 34% compared to the GAs in the baseline and the climate change periods, respectively.

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Data availability

Some or all data, models or code that support the findings of this study are available from the corresponding author upon reasonable request. (Case study data, MPA code and applied simulation models are available.)

References

  • Akbari-Alashti, H., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Multi-reservoir 1real-time operation rules: A new genetic programming approach. Proceedings of the Institution of Civil Engineers: Water Management, 167(10), 561–576.

    Google Scholar 

  • Akbarifard, S., Sharifi, M. R., & Qaderi, K. (2020). Data on optimization of the karun-4 hydropower reservoir operation using evolutionary algorithms. Data in Brief, 29, 105048.

    Article  Google Scholar 

  • Ashofteh, P.-S., Bozorg-Haddad, O., & Mariño, M. A. (2013a). Climate change impact on reservoir performance indexes in agricultural water supply. Journal of Irrigation and Drainage Engineering, 139(2), 85–97.

    Article  Google Scholar 

  • Ashofteh, P.-S., Bozorg-Haddad, O., & Mariño, M. A. (2013b). Scenario assessment of streamflow simulation and its transition probability in future periods under climate change. Water Resources Management, 27(1), 255–274.

    Article  Google Scholar 

  • Azadi, F., Ashofteh, P. S., & Chu, X. (2021a). Evaluation of the effects of climate change on thermal stratification of reservoirs. Sustainable Cities and Society, 66, 102531.

    Article  Google Scholar 

  • Azadi, F., Ashofteh, P.-S., Shokri, A., & Loáiciga, H. A. (2021b). Simulation-optimization of reservoir water quality under climate change. Journal of Water Resources Planning and Management, 147(9), 04021054.

    Article  Google Scholar 

  • Azizipour, M., Ghalenoei, V., Afshar, M. H., & Solis, S. S. (2016). Optimal operation of hydropower reservoir systems using weed optimization algorithm. Water Resources Management, 30(11), 3995–4009.

    Article  Google Scholar 

  • Bahrami, M., Bozorg-Haddad, O., & Chu, X. (2018). Application of cat swarm optimization algorithm for optimal reservoir operation. Journal of Irrigation and Drainage Engineering. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001256

    Article  Google Scholar 

  • Bashiri-Atrabi, H., Qaderi, K., Rheinheimer, D. E., & Sharifi, E. (2015). Application of harmony search algorithm to reservoir operation optimization. Water Resources Management, 29(15), 5729–5748.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Azad, M., Fallah-Mehdipour, E., Delpasand, M., & Chu, X. (2021). Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems’ operations considering their specific purposes. Water Supply, 21(1), 166–188.

    Article  CAS  Google Scholar 

  • Bozorg-Haddad, O., Janbaz, M., & Loáiciga, H. A. (2016). Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. Advances in Water Resources, 98, 173–185. https://doi.org/10.1016/j.advwatres.2016.11.001

    Article  Google Scholar 

  • Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015a). Development and application of the bat algorithm for optimizing the operation of reservoir systems. Journal of Water Resources Planning and Management, 141(8), 04014097.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Moravej, M., & Loáiciga, H. A. (2015b). Application of the water cycle algorithm to the optimal operation of reservoir systems. Journal of Irrigation and Drainage Engineering, 141(5), 04014064.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Solgi, M., & Loáiciga, H. (2017). Meta-heuristic and evolutionary algorithms for engineering optimization. Wiley.

    Book  Google Scholar 

  • Choi, Y., Ji, J., Lee, E., Lee, S., Yi, S., & Yi, J. (2023). Develo** optimal reservoir rule curve for hydropower reservoir with an add-on water supply function using improved grey wolf optimizer. Water Resources Management, 37, 2063–2082.

    Article  Google Scholar 

  • Donyaii, A., Sarraf, A., & Ahmadi, H. (2021). Optimization of reservoir dam operation using gray wolf, crow search and whale algorithms based on the solution of the nonlinear programming model. Journal of Water and Soil Science, 24(4), 159–175.

    Google Scholar 

  • Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.

    Article  Google Scholar 

  • Garousi-Nejad, I., Bozorg-Haddad, O., & Loáiciga, H. A. (2016). Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. Journal of Water Respurces Planning and Management, 142(9), 04016029.

    Article  Google Scholar 

  • Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.

    Article  Google Scholar 

  • Hınçal, O., Altan-Sakarya, A. B., & Ger, A. M. (2011). Optimization of multireservoir systems by genetic algorithm. Water Resources Management, 25(5), 1465–1487.

    Article  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. The University of Michigan Press.

    Google Scholar 

  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–72.

    Article  Google Scholar 

  • Jothiprakash, V., & Shanthi, G. (2006). Single reservoir operating policies using genetic algorithm. Water Resources Management, 20(6), 917–929.

    Article  Google Scholar 

  • Liu, D., Huang, Q., Yang, Y., Liu, D., & Wei, X. (2020). Bi-objective algorithm based on NSGA-II framework to optimize reservoirs operation. Journal of Hydrology, 585, 124830.

    Article  Google Scholar 

  • Louati, M. H., Benabdallah, S., Lebdi, F., & Milutin, D. (2011). Application of a genetic algorithm for the optimization of a complex reservoir system in Tunisia. Water Resources Management, 25(10), 2387–2404.

    Article  Google Scholar 

  • Nematollahi, Z., & Zarif Sanayei, H. R. (2023). Develo** an optimized groundwater exploitation prediction model based on the Harris hawk optimization algorithm for conjunctive use of surface water and groundwater resources. Environmental Science and Pollution Research, 30, 16120–16139.

    Article  CAS  Google Scholar 

  • Niu, W. J., Feng, Z. K., Liu, S., Chen, Y. B., Xu, Y. S., & Zhang, J. (2021). Multiple hydropower reservoirs operation by hyperbolic grey wolf optimizer based on elitism selection and adaptive mutation. Water Resources Management, 35(2), 573–591.

    Article  Google Scholar 

  • Rahmati, K., Ashofteh, P.-S., & Loáiciga, H. A. (2021). Application of the grasshopper optimization algorithm (goa) to the optimal operation of hydropower reservoir systems under climate change. Water Resources Management, 35, 4325–4348.

    Article  Google Scholar 

  • Rani, D., Pant, M., & Jain, S. K. (2020). Dynamic programming integrated particle swarm optimization algorithm for reservoir operation. International Journal of System Assurance Engineering and Management, 11(2), 515–529.

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, University of Qom, Qom, Iran

    Shirin Moradi-Far & Parisa-Sadat Ashofteh

  2. Department of Geography, University of California, Santa Barbara, CA, 93016-4060, USA

    Hugo A. Loáiciga

Authors
  1. Shirin Moradi-Far
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  2. Parisa-Sadat Ashofteh
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  3. Hugo A. Loáiciga
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Correspondence to Parisa-Sadat Ashofteh.

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Moradi-Far, S., Ashofteh, PS. & Loáiciga, H.A. Development of the marine predators algorithm for optimizing the performance of water supply reservoirs. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-023-04450-z

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