Abstract
Recently, there has been a growing interest in employing optimization techniques to ascertain the most efficient operation of reservoirs. This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). Each algorithm was integrated into a reservoir simulation model, focusing on finding optimal rule curves for the Mujib reservoir in Jordan from 2004 to 2019. The primary objective was to evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir. Furthermore, the study aimed to determine the effects of water demand management by reducing it by 10%, 20%, and 30%. The results revealed that the used algorithms effectively mitigated water shortages and excess releases compared to the current operational strategy. Notably, the Teaching Learning-Based Optimization (TLBO) algorithm yielded the most favorable outcomes, reducing the frequency and average of water shortages to 55.09% and 56.26%, respectively. Additionally, it curtailed the frequency and average of excess releases to 63.16% and 73.31%, respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of Data and Materials
Some data is available from the corresponding author upon request.
References
Afzalsoltani F, Yazdi J (2023) Development of a model for sediment evacuation from reservoirs. Environ Process 10(3):46. https://doi.org/10.1007/s40710-023-00660-9
Ahmadianfar I, Khajeh Z, Asghari-Pari SA, Chu X (2019) Developing optimal policies for reservoir systems using a multi-strategy optimization algorithm. Appl Soft Comput J 80:888–903. https://doi.org/10.1016/j.asoc.2019.04.004
Almubaidin MAA, Ahmed AN, Sidek LBM, Elshafie A (2022) Using metaheuristics algorithms (MHAs) to optimize water supply operation in reservoirs: a review. Arch Comput Methods Eng 29(6):3677–3711. https://doi.org/10.1007/s11831-022-09716-9
Almubaidin MA, Latif SD, Balan K, Ahmed AN, El-Shafie A (2023) Enhancing sediment transport predictions through machine learning-based multi-scenario regression models. Results Eng 101585. https://doi.org/10.1016/j.rineng.2023.101585
Al-Mubaidin M, Al-Hamaiedeh H, El-Hasan T (2022) Impact of the effluent characteristics of industrial and domestic wastewater treatment plants on the irrigated soil and plants. Jordan J Earth Environ Sci 13(3)
Asgari H-R, Bozorg Haddad O, Pazoki M, Loáiciga HA (2016) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 142(2):04015055. https://doi.org/10.1061/(asce)ir.1943-4774.0000963
Azizipour M, Ghalenoei V, Afshar MH, Solis SS (2016) Optimal operation of hydropower reservoir systems using weed optimization algorithm. Water Resour Manag 30(11):3995–4009. https://doi.org/10.1007/s11269-016-1407-6
Bashiri-Atrabi H, Qaderi K, Rheinheimer DE, Sharifi E (2015) Application of harmony search algorithm to reservoir operation optimization. Water Resour Manag 29(15):5729–5748. https://doi.org/10.1007/s11269-015-1143-3
Beça P, Rodrigues AC, Nunes JP, Diogo P, Mujtaba B (2023) Optimizing reservoir water management in a changing climate. Water Resour Manag 37(9):3423–3437. https://doi.org/10.1007/s11269-023-03508-x
Candau F, Regnacq C, Schlick J (2022) Climate change, comparative advantage and the water capability to produce agricultural goods. World Dev 158:105963. https://doi.org/10.1016/j.worlddev.2022.105963
Celeste AB, Billib M (2009) Evaluation of stochastic reservoir operation optimization models. Adv Water Resour 32(9):1429–1443. https://doi.org/10.1016/j.advwatres.2009.06.008
Chiamsathit C, Adeloye AJ, Soundharajan B (2015) A New Dynamic Genetic Algorithm for optimising the reservoir operating rule curves: A case study of the Ubonrantana reservoir, Thailand. XV World Water Congr 267–283
Chong KL, Lai SH, Ahmed AN, Wan Jaafar WZ, El-Shafie A (2021) Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm. Appl Soft Comput 106:107325. https://doi.org/10.1016/j.asoc.2021.107325
Giuliani M, Lamontagne JR, Reed PM, Castelletti A (2021) A state-of-the-art review of optimal reservoir control for managing conflicting demands in a changing world. Water Resour Res 57(12):e2021WR029927
Heris SMK (2022) Yarpiz-Academic Source Codes and Tutorials. https://yarpiz.com/. Accessed 1 May 2022
Ijam AZ, Al-Nawiseh AN, Ktishat K (2020) Storage reduction of Mujeb Dam reservoir in Jordan due to sedimentation. Methodology 10(6). https://doi.org/10.7176/jees/10-6-10
Kangrang A, Compliew S, Chaiyapoom W (2009) Heuristic algorithm with simulation model for searching optimal reservoir rule curves. Am J Appl Sci 6(2):263–267. https://doi.org/10.3844/ajas.2009.263.267
Kangrang A, Prasanchum H, Hormwichian R (2018) Development of future rule curves for multipurpose reservoir operation using conditional genetic and tabu search algorithms. Adv Civil Eng. https://doi.org/10.1155/2018/6474870
Khajvand M, Mostafazadeh AK, Drogui P, Tyagi RD (2022) Management of greywater: environmental impact, treatment, resource recovery, water recycling, and decentralization. Water Sci Technol 86(5):909–937. https://doi.org/10.2166/wst.2022.226
Kumar V, Yadav SM (2018) Optimization of reservoir operation with a new approach in evolutionary computation using TLBO algorithm and jaya algorithm. Water Resour Manag 32(13):4375–4391. https://doi.org/10.1007/s11269-018-2067-5
Ngoc TA, Hiramatsu K, Harada M (2014) Optimizing the rule curves of multi-use reservoir operation using a genetic algorithm with a penalty strategy. Paddy Water Environ 12(1):125–137. https://doi.org/10.1007/s10333-013-0366-2
Prasanchum H, Kangrang A (2018) Optimal reservoir rule curves under climatic and land use changes for Lampao Dam using genetic algorithm. KSCE J Civ Eng 22(1):351–364. https://doi.org/10.1007/s12205-017-0676-9
Rahimi H, Ardakani MK, Ahmadian M, Tang X (2020) Multi-reservoir utilization planning to optimize hydropower energy and flood control simultaneously. Environ Process 7(1):41–52. https://doi.org/10.1007/s40710-019-00404-8
Thongwan T, Kangrang A, Prasanchum H (2019) Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation. Heliyon 5(9)
Tung C, Hsu S, Liu C-M, Li J-S (2003) Application of the genetic algorithm for optimizing operation rules of the liyutan reservoir in Taiwan 1. JAWRA Jo Am Water Resour Assoc 39(3):649–657. https://doi.org/10.1111/j.1752-1688.2003.tb03682.x
Vranayová Z, Káposztásová D (2018) Water demand management and its impact on water resources at the building level. Water Resources in Slovakia: Part II: Climate Change, Drought and Floods 225–249
Walker WE, Loucks DP, Carr G (2015) Social Responses to Water Management Decisions. Environ Process 2(3):485–509. https://doi.org/10.1007/s40710-015-0083-5
Funding
This work was supported by the Ministry of Higher Education, Malaysia, through the Fundamental Research Grant Scheme (FRGS), under the project code of FRGS/1/2020/TK0/UNITEN/02/16.
Author information
Authors and Affiliations
Contributions
M.Almubaidin, A.Ahmed, and A.El-Shafie contributed to the study conception and design. Data collection was performed by M.Almubaidin and K.AL-Assifeh. Analyses were performed by M.Almubaidin. The first draft of the manuscript was written by M.Almubaidin, A. Ahmed, and A.El-Shafie who commented on previous versions of the manuscript. M.Almubaidin, A. Ahmed, A.El-Shafie, and L.Sidek read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical Approval
The authors undertake that this article has not been published in any other journal and that no plagiarism has occurred.
Consent to Participate
Not applicable.
Consent to Publish
Not applicable.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Highlights
• Employ advanced algorithms for efficient reservoir system operation strategies.
• Evaluate algorithms' efficiency in determining optimal rule curves—informing decision-making precision.
• Evaluate rules' effectiveness in mitigating drought and flood scenarios.
• Modelling Inflow, Evaporation, Release, and Seepage for comprehensive system understanding.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Almubaidin, M.A., Ahmed, A.N., Sidek, L.M. et al. Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms. Water Resour Manage 38, 1207–1223 (2024). https://doi.org/10.1007/s11269-023-03716-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-023-03716-5