Optimal Operation of Dam Reservoir Using Gray Wolf Optimizer Algorithm (Case Study: Urmia Shaharchay Dam in Iran)

Document Type : Regular Article

Authors

1 Ph.D. Candidate of Irrigation and Drainage, Department of Water Engineering, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, Iran

2 Ph.D. Candidate of Hydraulic Structures, Department of Water Engineering, University of Tabriz, Tabriz, Iran

Abstract

Reservoir storage prediction is so crucial for water resources planning and managing water resources, drought risk management and flood predicting throughout the world. In this study, Gray Wolf Optimizer algorithm (GWO) was applied to predict Shaharchay dam reservoir storage of located in the Urmia Lake basin, northwest of Iran. The results of the GWO algorithm have been compared with the continuous genetic algorithm (CGA). The predicted values from the GWO algorithm matched the measured values very well. According to the results, the error is not significant (2.11%) in the implementation of the GWO and the correlation coefficient between the predicted and measured values is 0.92. In addition, the statistical criteria of RMSE, MAE and NSE for GWO algorithm were estimated to be 0.03, 0.41 and 0.74, respectively, indicated a satisfactory performance. Excessive value of correlation coefficient expresses that the GWO algorithm pretty suit the variables and may finally be used for predicting of reservoir storage for operational overall performance. Comparison of results showed that the GWO algorithm with average best objective function value of 121, 112 and 83.10 with a number of further evaluations of the objective function to achieve higher capacity is the optimum answer.

Highlights

Google Scholar

Keywords

Main Subjects

References

[1]       Kilinç I, Cigizoglu K. Reservoir management using artificial neural networks. Proc 14th Reg Dir DSI (State Hydraul Work Istanbul, Turkey 2005.
[2]       Pilpayeh A, Sedghi H, Fahmi H, Jahromi HM. An optimizing operational model for multiobjective serial reservoirs (case study of Aras River Basin, Northwestern Iran). World Appl Sci J 2010;10:234–41.
[3]       Nagesh Kumar D, Janga Reddy M. Multipurpose Reservoir Operation Using Particle Swarm Optimization. J Water Resour Plan Manag 2007;133:192–201. doi:10.1061/(ASCE)0733-9496(2007)133:3(192).
[4]       Afshar A, Bozorg Haddad O, Mariño MA, Adams BJ. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J Franklin Inst 2007;344:452–62. doi:10.1016/j.jfranklin.2006.06.001.
[5]       Ahmadian MR, Farid-hosseini AR, Hojjati A. Multi-objective optimization using Imperialist Competitive algorithm. 5th Natl Water Resour Manag Conf Tehran, Iran Water Resour Sci Eng Soc ShahidBeheshti Univ, 2013.
[6]       Karamouz M, Nazif S, Sherafat MA, Zahmatkesh Z. Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study. Water Resour Manag 2014;28:3539–54. doi:10.1007/s11269-014-0686-z.
[7]       Izadbakhsh MA, Javadikia H. Application of hybrid FFNN-genetic algorithm for predicting evaporation in storage dam reservoirs. Agric Commun 2014;2:57–62.
[8]       Adeyemo J, Stretch D. Review of hybrid evolutionary algorithms for optimizing a reservoir. South African J Chem Eng 2018;25:22–31. doi:10.1016/j.sajce.2017.11.004.
[9]       Parlikar AS, Dahe PD, Vaidya M, Sharma K. Reservoir Releases using Genetic Algorithm. Int J Innov Adv Comput Sci 20127;6:363–6.
[10]     Prasanchum H, Kangrang A. Optimal reservoir rule curves under climatic and land use changes for Lampao Dam using Genetic Algorithm. KSCE J Civ Eng 2018;22:351–64. doi:10.1007/s12205-017-0676-9.
[11]     Kangrang A, Prasanchum H, Hormwichian R. Development of Future Rule Curves for Multipurpose Reservoir Operation Using Conditional Genetic and Tabu Search Algorithms. Adv Civ Eng 2018;2018:1–10. doi:10.1155/2018/6474870.
[12]     Anand J, Gosain AK, Khosa R. Optimisation of multipurpose reservoir operation by coupling SWAT and genetic algorithm for optimal operating policy (case study: Ganga River basin) 2018.
[13]     Cuvelier T, Archambeau P, Dewals B, Louveaux Q. Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty. Water Resour Manag 2018;32:1599–614. doi:10.1007/s11269-017-1893-1.
[14]     Olukanni DO, Adejumo TA, Salami AW, Adedeji AA. Optimization-based reliability of a multipurpose reservoir by Genetic Algorithms: Jebba Hydropower Dam, Nigeria. Cogent Eng 2018;5. doi:10.1080/23311916.2018.1438740.
[15]     Rabiei MH, Aalami MT, Talatahari S. Reservoir operation optimization using CBO, ECBO and VPS algorithms. Int J Optim Civ Eng 2018;8:489–509.
[16]     Kilinç I, Ciğizoğlu K. Reservoir management using artificial neural networks. 14th Reg Dir DSI (State Hydraul Work Istanbul, Turkey 2012.
[17]     www.agrw.ir n.d.
[18]     Mirjalili S, Mirjalili SM, Lewis A, Optimizer GW. Advances in engineering software. Renew Sustain Energy Rev 2014:46–61.
[19]     Mech LD. Alpha status, dominance, and division of labor in wolf packs. Can J Zool 1999;77:1196–203. doi:10.1139/z99-099.
[20]     Muro C, Escobedo R, Spector L, Coppinger RP. Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Processes 2011;88:192–7. doi:10.1016/j.beproc.2011.09.006.
[21]     Goldberg DE. GeneticA lgorithm in Search, Optim ization andM achineLearning. Read Addison2wes2 Ley 1989.
[22]     Ghorbani MA, Shamshirband S, Zare Haghi D, Azani A, Bonakdari H, Ebtehaj I. Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 2017;172:32–8. doi:10.1016/j.still.2017.04.009.
[23]     Saber Chenari K, Abghari H, Erfanian M, Ghaderi M, Salmani H, Asadi Nalivan O. Optimization Reservoir Operation Policy with Approach reduces probability of inflow Using Genetic Algorithm (The case study: Mahabad Reservoir Dam). Water Shed Manag Res 2016;29:34–43.

Files

Cited by

History

  • Receive Date: 10 June 2019
  • Revise Date: 05 January 2020
  • Accept Date: 05 January 2020

Share

How to cite

Statistics