Operating a reservoir system based on the shark machine learning algorithm

  • Mohammed Falah Allawi
  • Othman Jaafar
  • Firdaus Mohamad Hamzah
  • Mohammad Ehteram
  • Md. Shabbir Hossain
  • Ahmed El-Shafie
Original Article


The operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary algorithms, namely, the genetic algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand).


Semi-arid region Water deficit Water release Aswan High Dam 



The research is funded by the “Bold Grant” of University Tenaga Nasional (10289176/B/9/2017/57) and University of Malaya Research Grant “UMRG” (RP025A-18SUS). The authors are grateful to the UNITEN and Institute of Energy Infrastructure and University of Malaya, Malaysia for supporting the study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. Abedinia O, Amjady N, Ghasemi A (2014) A new metaheuristic algorithm based on shark smell optimization. Complexity 21:97–116. CrossRefGoogle Scholar
  2. Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Resour Manag 30:4773–4788. CrossRefGoogle Scholar
  3. Allawi MF, Jaafar O, Mohamad Hamzah F et al (2017) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor Appl Climatol. Google Scholar
  4. Allawi MF, Jaafar O, Mohamad Hamzah F et al (2018) Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environ Sci Pollut Res. Google Scholar
  5. Asgari H, Haddad OB, Pazoki M (2015) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng. Google Scholar
  6. Chang LC (2008) Guiding rational reservoir flood operation using penalty-type genetic algorithm. J Hydrol 354:65–74. CrossRefGoogle Scholar
  7. Chang FJ, Chen L, Chang LC (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19:2277–2289. CrossRefGoogle Scholar
  8. Chen D, Chen Q, Leon AS, Li R (2016a) A genetic algorithm parallel strategy for optimizing the operation of reservoir with multiple eco-environmental objectives. Water Resour Manag 30:2127–2142. CrossRefGoogle Scholar
  9. Chen S, Shao D, Li X, Lei C (2016b) Simulation-optimization modeling of conjunctive operation of reservoirs and ponds for irrigation of multiple crops using an improved artificial bee colony algorithm. Water Resour Manag 30:2887–2905. CrossRefGoogle Scholar
  10. Clarke J, McLay L, McLeskey JT (2014) Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant. Adv Eng Inform 28:81–90. CrossRefGoogle Scholar
  11. Ehteram M, Allawi M, Karami H, Mousavi S (2017) Optimization of chain-reservoirs’ operation with a new approach in artificial intelligence. Water Resour 31:2085–2104Google Scholar
  12. El-Shafie AH, El-Manadely MS (2010) An integrated neural network stochastic dynamic programming model for optimizing the operation policy of Aswan High Dam. Hydrol Res 42:50. CrossRefGoogle Scholar
  13. El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556. CrossRefGoogle Scholar
  14. El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23:2289–2315. CrossRefGoogle Scholar
  15. Fayaed SS, El-Shafie A, Jaafar O (2013) Integrated artificial neural network (ANN) and stochastic dynamic programming (SDP) model for optimal release policy. Water Resour Manag 27:3679–3696. CrossRefGoogle Scholar
  16. Gardiner JM, Atema J (2010) The function of bilateral odor arrival time differences in olfactory orientation of sharks. Curr Biol 20:1187–1191. CrossRefGoogle Scholar
  17. Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning and management. Water Resour Manag 21:1553–1566. CrossRefGoogle Scholar
  18. Hakimi-Asiabar M, Ghodsypour SH, Kerachian R (2010) Deriving operating policies for multi-objective reservoir systems: application of self-learning genetic algorithm. Appl Soft Comput J 10:1151–1163. CrossRefGoogle Scholar
  19. Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18:14–20. CrossRefGoogle Scholar
  20. Hınçal O, Altan-Sakarya AB, Metin Ger A (2011) Optimization of multireservoir systems by genetic algorithm. Water Resour Manag 25:1465–1487. CrossRefGoogle Scholar
  21. Hofmeyer H, Davila Delgado JM (2013) Automated design studies: topology versus one-step evolutionary structural optimisation. Adv Eng Inform 27:427–443. CrossRefGoogle Scholar
  22. Hossain MS, El-shafie A (2014) Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam. Neural Comput Appl 24:1199–1206. CrossRefGoogle Scholar
  23. Javadi AA, Farmani R, Tan TP (2005) A hybrid intelligent genetic algorithm. Adv Eng Inform 19:255–262. CrossRefGoogle Scholar
  24. Karamouz M, Goharian E, Nazif S (2013) Reliability assessment of the water supply systems under uncertain future extreme climate conditions. Earth Interact. Google Scholar
  25. Keshtegar B, Allawi MF, Afan HA, El-Shafie A (2016) Optimized river stream-flow forecasting model utilizing high-order response surface method. Water Resour Manag 30:3899–3914. CrossRefGoogle Scholar
  26. Luo J, Chen C, Xie J (2015) Multi-objective immune algorithm with preference-based selection for reservoir flood control operation. Water Resour Manag 29:1447–1466. CrossRefGoogle Scholar
  27. Mourshed M, Shikder S, Price ADF (2011) Phi-array: a novel method for fitness visualization and decision making in evolutionary design optimization. Adv Eng Inform 25:676–687. CrossRefGoogle Scholar
  28. Moy W-S, Cohon JL, ReVelle CS (1986) A programming model for analysis of the reliability, resilience, and vulnerability of a water supply reservoir. Water Resour Res 22:489–498CrossRefGoogle Scholar
  29. Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42:965–997. CrossRefGoogle Scholar
  30. Qi Y, Bao L, Sun Y et al (2016) A memetic multi-objective immune algorithm for reservoir flood control operation. Water Resour Manag 30:2957–2977. CrossRefGoogle Scholar
  31. Reddy MJ, Nagesh Kumar D (2007) Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol Process 21:2897–2909. CrossRefGoogle Scholar
  32. SaberChenari K, Abghari H, Tabari H (2016) Application of PSO algorithm in short-term optimization of reservoir operation. Environ Monit Assess 188:667. CrossRefGoogle Scholar
  33. Sedki A, Ouazar D (2012) Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems. Adv Eng Inform 26:582–591. CrossRefGoogle Scholar
  34. Sfakiotakis M, Lane DM, Davies JBC (1999) Review of fish swimming modes for aquatic locomotion. IEEE J Ocean Eng 24:237–252. CrossRefGoogle Scholar
  35. Tayebiyan A, Mohammed Ali TA, Ghazali AH, Malek MA (2016) Optimization of exclusive release policies for hydropower reservoir operation by using genetic algorithm. Water Resour Manag 30:1203–1216. CrossRefGoogle Scholar
  36. Wafae EH, Driss O, Bouziane A, Hasnaoui MD (2016) Genetic algorithm applied to reservoir operation optimization with emphasis on the Moroccan context. In: 2016 3rd International conference on logistics operations management (GOL). IEEE, pp 1–4Google Scholar
  37. Wang W, Rivard H, Zmeureanu R (2005) An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Adv Eng Inform 19:5–23. CrossRefGoogle Scholar
  38. Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plan Manag 125:25–33.
  39. York C, Goharian E, Burian SJ (2015) Impacts of large-scale stormwater green infrastructure implementation and climate variability on receiving water response in the Salt Lake City Area. Am J Environ Sci 11:278–292. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mohammed Falah Allawi
    • 1
  • Othman Jaafar
    • 1
  • Firdaus Mohamad Hamzah
    • 1
  • Mohammad Ehteram
    • 2
  • Md. Shabbir Hossain
    • 3
  • Ahmed El-Shafie
    • 4
  1. 1.Civil and Structural Engineering Department, Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Water Engineering and Hydraulic Structures, Faculty of Civil EngineeringSemnan UniversitySemnanIran
  3. 3.Department of Civil EngineeringUniversiti Tenaga NasionalKajangMalaysia
  4. 4.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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