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Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs

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Abstract

Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (> 660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.

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References

  • Ahmadi M, Bozorg-Haddad O, Mariño MA (2014) Extraction of flexible multi-objective real-time reservoir operation rules. Water Resour Manag. https://doi.org/10.1007/s11269-013-0476-z

    Article  Google Scholar 

  • Alahdin S, Ghafouri HR, Haghighi A (2019) Multi-reservoir system operation in drought periods with balancing multiple groups of objectives. KSCE J Civ Eng 23(2):914–922. https://doi.org/10.1007/s12205-018-0109-4

    Article  Google Scholar 

  • Amirkhani M, Bozorg-Haddad O, Fallah-Mehdipour E, Loáiciga HA (2016) Multiobjective reservoir operation for water quality optimization. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001105

    Article  Google Scholar 

  • Azadi F, Ashofteh P-S, Chu X (2021a) Evaluation of the effects of climate change on thermal stratification of reservoirs. Sustain Cities Soc 66:102531. https://doi.org/10.1016/j.scs.2020.102531

    Article  Google Scholar 

  • Azadi F, Ashofteh P-S, Loáiciga HA (2019) Reservoir water-quality projections under climate-change conditions. Water Resour Manag 33(1):401–421. https://doi.org/10.1007/s11269-018-2109-z

    Article  Google Scholar 

  • Azadi F, Ashofteh P-S, Shokri A, Loáiciga HA (2021b) Simulation-optimization of reservoir water quality under climate change. J Water Resour Plan Manag 147(9)

  • Bozorg-Haddad O, Garousi-Nejad I, Loáiciga HA (2017) Extended multi-objective firefly algorithm for hydropower energy generation. J Hydroinf 19(5):734–751. https://doi.org/10.2166/hydro.2017.114

    Article  Google Scholar 

  • Chen H-T, Wang W-C, Chau K-W, Xu L, He J (2021) Flood control operation of reservoir group using Yin-Yang Firefly Algorithm. Water Resour Manag 35:5325–5345. https://doi.org/10.1007/s11269-021-03005-z

    Article  Google Scholar 

  • Coello C. A, Van Veldhuizen D. A, and Lamont G. B (2007) Evolutionary Algorithms for Solving Multi-Objective Problems. Springer Science+Business Media, New York, USA

  • Cole TM, Wells SA (2011) CE-QUAL-W2: A two-dimensional, laterally averaged, hydrodynamic and water quality model, version 3.7, Department of Civil and Environmental Engineering, Portland State University, Portland, OR

  • Deb K (1999) Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evol Comput 7(3):205–230. https://doi.org/10.1162/evco.1999.7.3.205

    Article  Google Scholar 

  • Deb K (2001) Multi-objective Optimization Using Evolutionary Algorithms. John Wiley and Sons Inc, New York, USA

    Google Scholar 

  • Deb K, Pratap A, Agrawal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. Transactions on Evolutionary Computation 6(2):182–197

    Article  Google Scholar 

  • Dhar A, Datta B (2007) Optimal operation of reservoirs for downstream water quality control using linked simulation optimization. Hydrol Process 22(6):842–853. https://doi.org/10.1002/hyp.6651

    Article  Google Scholar 

  • Fix E, Hodges JL (1951) Discriminatory analysis, nonparametric discrimination: Consistency properties. USAF School of Aviation Medicine, Randolph Field, Tex., Project 21–49–004, Rept. 4, Contract AF41(128)-31, February

  • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA (2016a) Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J Water Resour Plan Manag. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000644

    Article  Google Scholar 

  • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016b) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001064

    Article  Google Scholar 

  • Jahandideh-Tehrani M, Bozorg-Haddad O, Loáiciga HA (2020) A review of applications of animal-inspired evolutionary algorithms in reservoir operation modeling. Water and Environment Journal. https://doi.org/10.1111/wej.12657

    Article  Google Scholar 

  • Jalilian A, Heydari M, Azari A, and Shabanlou S (2022) Extracting Optimal Rule Curve of Dam Reservoir Base on Stochastic Inflow. Water Resour Manage 36:1763–1782. https://doi.org/10.1007/s11269-022-03087-3

  • Mitchell T (1997) Machine Learning. McGraw-Hill Science, New York, U.S.A

  • Ostfeld A, Salomons S (2005) A hybrid genetic—instance based learning algorithm for CE-QUAL-W2 calibration. J Hydrol 310(1–4):122–142

  • Pick T (2011) Assessing water quality for human consumption, agriculture, and aquatic life uses. United States Department of Agriculture, Environment Technical Note No. MT-1 (Rev. 2), June

  • Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48(7). https://doi.org/10.1029/2011WR011527

  • Saadatpour M, Afshar A, Edinger JE (2017) Meta-model assisted 2D hydrodynamic and thermal simulation model (CE-QUAL-W2) in deriving optimal reservoir operational strategy in selective withdrawal scheme. Water Resour Manage 31:2729–2744. https://doi.org/10.1007/s11269-017-1658-x

    Article  Google Scholar 

  • Saadatpour M, Javaheri Sh, Afshar A, Sandoval-Solis S (2021) Optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects. Expert Syst Appl 184:115474. https://doi.org/10.1016/j.eswa.2021.115474

    Article  Google Scholar 

  • Shenava N, Shourian M (2018) Optimal reservoir operation with water supply enhancement and flood mitigation objectives using an optimization-simulation approach. Water Resour Manag. https://doi.org/10.1007/s11269-018-2068-4

    Article  Google Scholar 

  • Shirangi E, Kerachian R, Bajestan MS (2008) A simplified model for reservoir operation considering the water quality issues: Application of the Young conflict resolution theory. Environ Monit Assess 146:77–89. https://doi.org/10.1007/s10661-007-0061-0

    Article  Google Scholar 

  • Shokri A, Bozorg-Haddad O, Mariño MA (2013) Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems. Water Resour Manag 27(7):2231–2249. https://doi.org/10.1007/s11269-013-0285-4

    Article  Google Scholar 

  • Soleimani S, Bozorg-Haddad O, Saadatpour M, Loáiciga HA (2016) Optimal Selective Withdrawal Rules Using a Coupled Data Mining Model and Genetic Algorithm. J Water Resour Plan Manag 142(2). https://doi.org/10.1061/%28ASCE%29WR.1943-5452.0000717

  • Srinivasan K, Kumar K (2018) Multi-objective simulation-optimization model for long-term reservoir operation using piecewise linear hedging rule. Water Resource Management 32(5):1901–1911. https://doi.org/10.1007/s11269-018-1911-y

    Article  Google Scholar 

  • USEPA (1986) Quality criteria for water, EPA Rep. 440/5–86–001. Washington, DC: USEPA

  • Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu Ph S, Zhou Z-H, Steinbach M, J. Hand D, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37. https://doi.org/10.1007/s10115-007-0114-2

    Article  Google Scholar 

  • Yang XS (2008) Firefly algorithm, Nature-inspired meta-heuristic algorithms. Wiley Online, Library 20:79–90

    Google Scholar 

  • Yang XS (2010) Firefly algorithm, levy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and Development in Intelligent Systems XXVI. Springer, pp 209–218

    Chapter  Google Scholar 

  • Yan S, Minsker B (2006) Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour Res. https://doi.org/10.1029/2005WR004303

  • Yosefipoor P, Saadatpour M, Solis SS, Afshar A (2022) An adaptive surrogate-based, multi-pollutant, and multi-objective optimization for river-reservoir system management. Ecol Eng 175:106487. https://doi.org/10.1016/j.ecoleng.2021.106487

    Article  Google Scholar 

  • Zhao J, Shen J, Cheng C, Guo Y (2017) Multi-objective optimization of multi-reservoir operation rules with controlling critical water levels. World Environmental and Water Resources Congress May 21–25, Sacramento, California, United States. https://doi.org/10.1061/9780784480595.045

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Authors and Affiliations

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

    Mahboubeh Khorsandi, Parisa-Sadat Ashofteh & Firoozeh Azadi

  2. Department of Civil, Construction and Environmental Engineering, North Dakota State University, Dept 2470, Fargo, ND, 58108-6050, USA

    Xuefeng Chu

Authors
  1. Mahboubeh Khorsandi
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  2. Parisa-Sadat Ashofteh
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  3. Firoozeh Azadi
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  4. Xuefeng Chu
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Contributions

M. Khorsandi developed the theory and performed the computations. F. Azadi verified the analytical methods. P.-S. Ashofteh and F. Azadi encouraged M. Khorsandi to investigate a specific aspect. P.-S. Ashofteh supervised the findings of this work, and F. Azadi, and X. Chu helped supervise the project. All authors discussed the results and contributed to the final manuscript. M. Khorsandi wrote the manuscript with support from P.-S. Ashofteh, F. Azadi, and especially X. Chu. P.-S. Ashofteh conceived the original idea.

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Correspondence to Parisa-Sadat Ashofteh.

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Khorsandi, M., Ashofteh, PS., Azadi, F. et al. Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs. Water Resour Manage 36, 3283–3304 (2022). https://doi.org/10.1007/s11269-022-03201-5

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