Using Shuffled Frog-Leaping Algorithm (SFLA) And Geospatial Information System (GIS) To Help Optimally Operation of The Dam Reservoir (Case Study: Dorudzan Dam Reservoir)

Document Type : Research Article


1 Graduate Student, Engineering and Water Resources Management, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

2 Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran


Freshwater resources are limited in the world and long-term exploitation will cause aridity or drought. Iran is geographically located in the dry and semi-arid region of the world, with an average rainfall of about one-third the world average. In this regard, increased competition for water, the need for food for the growing population and the increase in aridity in many regions, require the use of management practices to prevent water crisis and the resulting impacts. On the other hand, the consequences of successive droughts have increased the importance of proper water resources management. Considering the momentous role of reservoirs in supplying water needs for different sectors of consumption, optimal utilization of them is one of the important solutions to the problems of water resources and the lack of appropriate distribution of time and space. In this research, a Shuffled Frog-Leaping Algorithm is proposed as a meta-heuristic method for solving optimization problems in water resource systems, and a Geospatial Information System is used to optimize the operation of the Dorudzan Dam reservoir. The combination of these two to solve the dam reservoir optimization problem was first carried out in the country in this paper. The result of the research in the area indicated that the algorithm is well able to optimize and allocate the reservoir water downstream in such a way that 99.9% of the need for the downstream of the Dorudzan dam during the studied period, was provided for. Also, the combination of this algorithm with the Geospatial Information System provides an opportunity for a more precise examination of optimization results.


Main Subjects

[1]. Karamouz M, Ahmadi A, Nazif S. Challenges and opportunities for using optimal utilization models of water resources systems, 1st Conference on Optimum Utilization of Water Resources, 2006.[persian].
[2]. Weise T. Global optimization algorithms-theory and application. Self-Published Thomas Weise. 2009 Jun 26.
[3]. Sharma S, Sharma TK, Pant M, Rajpurohit J, Naruka B. Centroid mutation embedded shuffled frog-leaping algorithm. Procedia Computer Science. 2015;46:127-134.
[4]. Eusuff MM, Lansey KE. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources planning and management. 2003;129(3):210-225.
[5]. Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms. Advanced engineering informatics. 2005;19(1):43-53.
[6]. Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering optimization. 2006;38(2):129-154.
[7]. Luo XH, Yang Y, Li X. Solving TSP with shuffled frog-leaping algorithm. In2008 Eighth InternationalConference on Intelligent Systems Design and Applications 2008; 3: 228-232. IEEE.
[8]. Chung G, Lansey K. Application of the shuffled frog leaping algorithm for the optimization of a general large-scale water supply system. Water resources management. 2009;23(4):797-823.
[9]. Amiri B, Fathian M, Maroosi A. Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology. 2009;45(1-2):199-209.
[10]. Vafaeinejad A. Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS, Iranian Journal of Ecohydrology, 2016; 3(1): 69 – 82.[persian].
[11]. Vafaeinejad A, Yousefzadeh J, Yousefi H, Mohamadi Varzaneh N. Using GIS and linear programming to manage water distribution in irrigation networks and cropping pattern allocation (Case study: Downstream lands of Aq-chay Dam), Iranian Journal of Ecohydrology, 2014; 1(2): 123 – 132.[persian].
[12]. Mohamadi Varzaneh N, Vafaeinejad A. Water Allocation in Irrigation Networks by using of Decision Support System Based on the Geospatial Information System (GIS) and Particle Swarm Optimization (PSO), Iranian Journal of Ecohydrology, 2015; 2(1): 39 – 49.[persian].
[13]. Luo J, Chen MR. Improved shuffled frog leaping algorithm and its multi-phase model for multi-depot vehicle routing problem. Expert Systems with Applications. 2014;41(5):2535-2545.
[14]. Kennedy J, Eberhart R. Particle swarm optimization (PSO). InProc. IEEE International Conference on Neural Networks, Perth, Australia 1995;1942-1948.
[15]. Liping Z, Weiwei W, Yi H, Yefeng X, Yixian C. Application of shuffled frog leaping algorithm to an uncapacitated SLLS problem. AASRI Procedia. 2012;1:226-231.
[16]. Jaafari A, Zenner EK, Panahi M, Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and forest meteorology. 2019;266:198-207.
[17]. Yamani M, Moghimi E, Jodari-E-Eyvazi J, Mohamadi H, Issaee A. Effects of Ecogeomorphological Parameters on Chemical Water Quality Case Study: Kor River and Doroodzan Dam Lake. Geography and Environmental Planning, 2010; 21(1): 17 –32.[persian].
[18]. Hosseini Moghari S, Banihabib M. Optimizing operation of reservoir for agricultural water supply using firefly algorithm. Journal of Soil and Water Resources Conservation, 2014; 3(4): 17-31. [persian].
Volume 6, Issue 4
January 2020
Pages 983-991
  • Receive Date: 21 April 2019
  • Revise Date: 06 August 2019
  • Accept Date: 06 August 2019
  • First Publish Date: 22 December 2019
  • Publish Date: 22 December 2019