Volume 9, Issue 18 (1-2019)
jwmr 2019, 9(18): 168-177 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Azari A, Asadi M, Arman A. (2019). Comparing the Performance of Genetic and Particle Swarm Algorithms in Calibration of Experimental Area Reduction Method Based on Hydrographic Results of Dez Dam Reservoir. jwmr. 9(18), 168-177. doi:10.29252/jwmr.9.18.168
URL: http://jwmr.sanru.ac.ir/article-1-863-en.html
URL: http://jwmr.sanru.ac.ir/article-1-863-en.html
Razi University
Abstract: (2876 Views)
Area-volume-elevation (AVE) curves of dams pose one of the most important tools for water resource planning and reservoir management. Area reduction method is one of the methods which was developed to modify these curves after reservoir sedimentation based on the conditions, statistics and information recorded in reservoirs abroad; however the application of the same methods for domestic dams without optimizing the coefficients is not bug-free, and associates sometimes with a great deal of error. This study aims to calibrate automatically the area reduction method based on its three influencing parameters using two genetic and particle swarm optimization algorithms and to compare the results with reservoir hydrographic studies. So that, predicting the trend of reservoir sedimentation in area reduction method with its hydrographic results at the end of the period has the least difference. Results indicate the excellence of particle swarm algorithm in calibrating the area reduction method. In this algorithm, by selecting the initial population of 50, the time of convergence and the value of the objective function (RMSE of the predicted volume-elevation curve and actual reservoir) in the last iteration was 4.7 minutes and 7 MCM, which it represents 92.6% and 48% improvement, respectively compared with the genetic algorithm. Finally, the optimal values of area reduction method parameters were used to match further the estimated and actual volume values of Dez dam reservoir. The results revealed that the predicted error value is less than 1%, which is assessed negligible according to the volume of the reservoir. Accordingly, the developed model can be employed without any change in the optimal parameters of the area reduction method by entering information of reservoir’s new hydrography to predict the sedimentation trend during the coming years. This will be very helpful in light of the importance of knowing the reservoir’s useful volume variation in the coming years and its role in future water resource planning.
Keywords: Genetic algorithm, Particle swarm optimization, Area reduction method, Reservoir hydrography
Type of Study: Research |
Subject:
هيدرولوژی
Received: 2017/10/5 | Revised: 2019/01/21 | Accepted: 2018/08/27 | Published: 2019/01/21
Received: 2017/10/5 | Revised: 2019/01/21 | Accepted: 2018/08/27 | Published: 2019/01/21
References
1. Abedini, M. and N. Talebbidokhti. 1989. Distribution and control of sedimentation in dam reservoirs. First Iranian Conference on Hydrology. Mahab Ghods, Tehran, 791-820 pp (In Persian).
2. Alikhani, K., K. Qaderi and M. M. Ahmadi. 2013. Determination of optimal parameters of empirical area reduction method in Karaj dam sediment distribution. 9th International River Engineering Conference. Ahwaz, 8 pp (In Persian).
3. Azari, A., S. Soori and H. Bonakdari. 2017. The Application of Imperialist Competitive Algorithm in Determining the Optimal Parameters of Empirical Area Reduction Method to Predict the Sedimentation Process in Dez Dam. Geography and Sustainability of Environment, 7(24): 1-14 (In Persian).
4. Borland, W.M. and C.R. Miller. 1958. Distribution of sediment in large reservoirs. Journal of the Hydraulics Division -ASCE, 84: 1-18.
5. Cai, X., D.C. McKinney and L.S. Lasdon. 2001. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Advances in Water Resources, 24(6): 667-676. [DOI:10.1016/S0309-1708(00)00069-5]
6. Dankoo, A., J. Samani, M.Z. Ahmadi and A. Emadi. 2011. Uncertainty analysis for estimation of sediment volume in dam reservoirs (case study: amir kabir dam). Journal of Watershed Management Research, 1(2): 84-94 (In Persian).
7. Dariane, A.B. and S. Momtahen 2009. Optimization of multireservoir systems operation using modified direct search genetic algorithm. Journal of Water Resources Planning and Management, 135(3): 141-148. [DOI:10.1061/(ASCE)0733-9496(2009)135:3(141)]
8. Emadi, A.R. and S. Kakouei. 2014. Determination of Optimal Parameters of Empirical Area Reduction Method in Karaj Reservoir Dam using SCE, Water & Soil Conservation, 21: 179-195.
9. Emadi, A.R., M. Khademi and A. Mohamadiha. 2013. Application of Simulated Annealing Algorithm in Calibration of Area Reduction Method in Sediment Distribution of Dams Reservoir (Case Study: Karaj Dam), Water and Soil Conservation, 4: 173-188.
10. Emadi, A., A. Mohammadiha and J. Mohammad Vali Samani. 2011. Mathematical model for auto calibration of area-reduction method in sediment distribution of dam reservoir using genetic algorithm. Journal of Water and Soil. 25(2): 356-364 (In Persian).
11. Heydari, F., B. Saghafian and M. Delavar. 2016. Development of conjunctive surface and ground water use model with emphasis on the quality and quantity of water resources. Iranian Journal of Soil and Water Research, 47(4): 687-699 (In Persian).
12. Kennedy, J. and R. Eberhart. 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1942-1945.
13. Kennedy, J. and R.C. Eberhart (1997, October). A discrete binary version of the particle swarm algorithm. in systems, man and cybernetics, 1997. Computational Cybernetics and Simulation. 1997 IEEE International Conference, 5: 4104-4108 IEEE.
14. Kiafar, H., A.A. Sadraddini, A.H. Nazemi and H. Sanikhani. 2011. Optimal water allocation for Sufi-chay irrigation and drainage network in east azerbaijan province of iran using genetic algorithm. Journal of Irrigation & Water Engineering, 2(5): 52-61 (In Persian).
15. Lara, J. M. 1962. Revision of the procedure to compute sediment distribution in large reservoirs, US Bureau of Reclamation, Denver, Colorado.
16. Meskar, H. and R. Fazl-oula. 2013. Investigation of sedimentation pattern in the shahid rajaee reservoir using GSTAR3.0 numerical model. Journal of Watershed Management Research, 4(7): 16-29 (In Persian).
17. Mousavi, S.F., M. Haidarpour and S. Shabanlou. 2007. Investigation of sediments in the zayandehrud reservoir through areaincrement and areareduction empirical models. Journal of Water Wastewater, 57: 76-82 (In Persian).
18. Mousavi, S., J. Mohammadzadeh Habili and M. Heidarpour. 2009. Evaluation of error in area-increment and area-reduction methods to predict sediment distribution of Dez, Dorudzan and Shahid abbaspour reservoirs. Journal of Water and Soil Science, 12(45): 553-564 (In Persian).
19. Mousavi Rastegar, Z., A. Mohammadreza Poor, O. Bozorg Haddad and M. Ibrahimi. 2017. Comparison of hedging policy using metahuristic algorithm and standard operation policy in optimal operation of voshmgir reservoir dam in during drought. Iranian Journal of Soil and Water Research, 48(2): 323-333 (In Persian).
20. Noruzi, B., Gh.A. Barani, M. Meftah Halaghi and A.A. Dehghani. 2011. A multi-reservoir system operation optimization using multi population genetic algorithms (case study:golestan and voshmgir reservoirs). Journal of Water and Soil Conservation, 18(4): 43-62 (In Persian).
21. Pour Bourjarian, A. and M.A. Bani Hashemi. 2009. Investigation of sefidrood dam reservoir sedimentation trend using experimental and numerical relations and methods. M.Sc. Thesis, Faculty of Engineering, University of Tehran, Tehran, Iran (In Persian).
22. Shabanloo, S., S.F. Mousavi and M. Heidarpour. 2002. Assessing the amount of sediment entering Dez reservoir and its distribution pattern so far and estimating reservoir status in the future. 6th International River Engineering Congress, Feb. 2002, Shahid Chamran University, Ahwaz, 55-65 (In Persian).
23. Shi, Y. and R. C. Eberhart. 1998. Parameter selection in particle swarm optimization. In International Conference on Evolutionary Programming, 591-600. Springer Berlin Heidelberg. [DOI:10.1007/BFb0040810]
24. Strand, R.I. and E.L. Pemberton. 1982. Reservoir sedimentation. US Bureau of Reclamation, Denver.
25. Tulabi, S., M. Abedini and A. Esmali Ouri. 2015. The Evaluation efficiency of WEPP model to predict sediment yield in sulachai watershed-ardabil. Journal of Watershed Management Research, 6(12): 184-192 (In Persian).
26. United States Bureau of Reclamation. 1962. Revision of the procedure to compute sediment distribution in large reservoirs. Sedimentation Section, Hydrology Branch.
27. Vasan, A. 2013. Optimal reservoir operation for irrigation planning using the swarm intelligence algorithm. Metaheuristics in Water, Geotechnical and Transport Engineering, 147-165. [DOI:10.1016/B978-0-12-398296-4.00007-6]
28. Verstraeten, G., J. Poesen, J. de Vente and X. Koninckx. 2003. Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates, Geomorphology, 50(4): 327-348. [DOI:10.1016/S0169-555X(02)00220-9]
29. Vose, M.D. 1999. The simple genetic algorithm: foundations and theory, MIT Press, Cambridge, MA. 251 pp.
30. Zhang, J., Z. Wu, C.T. Cheng and S.Q. Zhang. 2011. Improved particle swarm optimization algorithm for multi-reservoir system operation. Water Science and Engineering, 4(1): 61-74.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
