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.

Keywords

Main Subjects

References

[1] Kilinç, I., Ciğizoğlu, K. Reservoir Management Using Artificial Neural Networks. State Hydraulic Works. (2003).

[2] Pilpayesh, A., Sedghi, H., Fahmi, H. and MusaviJahromi, H. An optimization operational model for multiobjective serial reservoirs (Case study ofAras river basin, northwestern Iran). World Applied Science Journal. 10 (2) (2010), 234-241.

[3] Nagesh Kumar, D., Janga Reddy, M. Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management. 133 (3) (2007) DOI: 10.1061/(ASCE)0733-9496(2007)133:3(192).

[4] Afshar, A., Bozorg Haddad, O., Mariño, M. A. and Adams, B. J. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute. 344 (5) (2007), 452–462.

[5] Ahmadian, M. R., Farid-hosseini, A. R. and Hojjati, A. Multi-objective optimization using Imperialist Competitive algorithm. 5th National Water Resources Management Conference, Tehran, Iran Water Resources Science and Engineering Society, ShahidBeheshti University. 2013.

[6] Karamouz, M., Nazif, S., Sherafat, M. A. and Zahmatkesh, Z. Development of an OptimalReservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based onConflict Resolution Approach: A Case Study, Water Resources Management, 28 (2014), 3539-54.

[7] Izadbakhsh, M. A. and Javadikia, H. Application of hybrid FFNN-Genetic algorithm for predicting evaporation in storage dam reservoirs. Agricultural Communications. 2(4) (2015), 57-62.

[8] Adeyemo, J. and Stretch, D. 2018. Review of hybrid evolutionary algorithms foroptimizing a reservoir. southafrican journal of chemical engineering, 25: 22-31.

[9] Parlikar, A. S., Dahe, P. D., Vaidya, M. and Sharma, K. Reservoir Releases using Genetic Algorithm. International Journal of Innovations & Advancement in Computer Science. 6 (11) (2017), 363-366.

[10] Prasanchum, H. and Kangrang, A. Optimal reservoir rule curves under climatic and land use changes for Lampao dam using Genetic algorithm. KSCE Journal of Civil Engineering. (2017), 1-14.

[11] Kangrang, A., Prasanchum, H. and Hormwichian, R. Development of future rule curves for multipurpose reservoir operation using conditional Genetic and Tabu search algorithms. Advances in Civil Engineering, Volume 2018, Article ID 6474870, (2018), 10 pages.

[12] Anand, J., Gosain, A. K. and Khosa, R. Optimisation of multipurpose reservoir operation by coupling SWAT and Genetic algorithm for optimal operating policy (Case Study: Ganga river basin). Preprints, doi:10.20944/preprints201803.0199.v1. 2018.

[13] Cuvelier, Th., Archambeau, P., Dewals, B. and Louveaux, Q. Comparison between robust and stochastic optimisation for long-term reservoir management under uncertainty. Water Resources Management, (2018), 10.1007/s11269-017-1893-1.

[14] Olukanni, D. O., Adejumo, T. A., Salami, A. W. and Adedeji, A. A. Optimization-based reliability of a multipurpose reservoir by Genetic algorithms: Jebba hydropower dam, Nigeria. Cogent Engineering, doi.org/10. (2018), 1080/23311916.2018.1438740.

[15] Rabiei, M. H., Aalami, M. T. and Talatahari, S. Reservoir operation optimization using CBO, ECBO and VPS algorithms. International Journal of Optimization in Civil Engineering. 8 (3) (2018), 489-509.

[16] Ismail, K. and Kerem, C. Reservoir Management Using Artificial Neural Networks. 14th. Reg. Directorate of DSI (State Hydraulic Works), Istanbul / Turkey. 2012.

[17] www.agrw.ir

[18] S. Mirjalili, S. M. Mirjalili A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, 69, 920140 46-61.

[19] L. D. Mech, Alpha Status, Dominance, and Division of Labor in Wolf Packs, Canadian Journal of Zoology, 77(8) (1999) 1196-1203.

[20] C. Muro, R. Escobedo, L. Spector and R. Coppinger, Wolf-pack (Canis Lupus) Hunting Strategies Emerge from Simple Rules in Computational Simulations, Behavioural Processes, 88(3) (2011) 192-197.

[21] Goldberg, D. E. Genetic algorithm in search, optimization and machine learning. Addisonwesley, Pub. Co. (1998), pp. 372.

[22] Gorbani, M. A., Shahabboddin, Sh., ZareHaghi, D., Azani, A., Bonakdari, H. and Ebtehaj, I. App;ication of Firefly Algorithm-Based Support Vector Machines for Prediction of Filed Capacity and Permanent Wilting Point. Soil and Tillage Research. 172 (2017): 32-38.

[23] Saber Chenari, K., Abghari, H., Erfanian, M., Ghaderi, M., Salmani, H., Asadi Nilvan, O. Optimization reservoir operation policy with approach reduces probability of inflow using genetic algorithm (the case study: Mahabad reservoir dam). Watershed Management Research. 111 (2016) 34-43.

Files

History

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

Share

How to cite

Statistics