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Uncertainty Analysis of Reservoir Operation Based on Stochastic Optimization Approach Using the Generalized Likelihood Uncertainty Estimation Method

Author

Listed:
  • Macdonald Tatenda Muronda

    (Bu-Ali Sina University)

  • Safar Marofi

    (Bu-Ali Sina University)

  • Hamed Nozari

    (Bu-Ali Sina University)

  • Omid Babamiri

    (Bu-Ali Sina University)

Abstract

This study evaluated the reservoir operation for effective water allocation under uncertainty using linear programming (the water evaluation and planning model: WEAP) and evolutionary optimization (the particle swarm optimization algorithm: PSOA). The stochastic autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models were used to forecast monthly inflow into the AmirKabir dam reservoir (Iran) as well as its net evaporation (2018-2025), respectively. For this purpose, the best-fitted model was selected based on the minimum Akaike criterion and the autocorrelation function (ACF) test. The resulting uncertainty of monthly release for the WEAP and PSOA were analyzed using the generalized likelihood uncertainty estimation method (GLUE). The results showed that the WEAP model proved to be better in meeting water demands during low inflows, whereas the PSOA had a higher certainty in meeting demands during high inflows periods. Also, the WEAP model had a high uncertainty in January-April compared to May-December, whereas the PSO algorithm had a high uncertainty in all months. This evaluation of water allocation considering uncertainties of fluctuating water supply and net evaporation helps us answer questions about optimal allocating of different demand sites: how is water shortage affects economic, social, environmental aspects, and the relationship between systems sustainability and water shortage.

Suggested Citation

  • Macdonald Tatenda Muronda & Safar Marofi & Hamed Nozari & Omid Babamiri, 2021. "Uncertainty Analysis of Reservoir Operation Based on Stochastic Optimization Approach Using the Generalized Likelihood Uncertainty Estimation Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3179-3201, August.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:10:d:10.1007_s11269-021-02877-5
    DOI: 10.1007/s11269-021-02877-5
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    References listed on IDEAS

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    1. E. Vonk & Y. Xu & M. Booij & X. Zhang & D. M. Augustijn, 2014. "Adapting Multireservoir Operation to Shifting Patterns of Water Supply and Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 625-643, February.
    2. Y. Mylopoulos & N. Theodosiou & N. Mylopoulos, 1999. "A Stochastic Optimization Approach in the Design of an Aquifer Remediation under Hydrogeologic Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(5), pages 335-351, October.
    3. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Abolfazl Baniasadi Moghadam & Hossein Ebrahimi & Abbas Khashei Siuki & Abolfazl Akbarpour, 2022. "Reliability-based Operation of Reservoirs Using Combined Monte Carlo Simulation Model and a Novel Nature-inspired Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4447-4468, September.
    2. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).

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