Gray Wolf Optimization for Scheduling Irrigation Water
Publication: Journal of Irrigation and Drainage Engineering
Volume 148, Issue 7
Abstract
Although various optimization algorithms have been developed for operational purposes, the development of new optimization algorithms is still an open problem due to the complex system of irrigation canals that must be addressed. Recently, a new algorithm named gray wolf optimization (GWO) has been introduced and applied in different contexts. It mimics the social hierarchy and hunting behavior of gray wolves in nature. In this research, GWO was formulated, developed, and linked to irrigation canal system simulation (ICSS) to schedule water delivery. A fitness (optimization) function was defined according to the standard water delivery performance indicators. Normal and water shortage operational scenarios in the E1R1 Dez canal in Iran were tested and evaluated. The results revealed that GWO is a powerful optimization method and avoids local optimal points when normal conditions exist. However, it has relatively poor performance in water shortage conditions in which there is not enough water. Water depth variations remain inside acceptable margins. Its results were comparable to fuzzy state, action, reward, state, action (SARSA) learning (FSL) in the same canal, showing a value of maximum absolute error (MAE) and integral absolute error (IAE) of 10.7% and 9.2%, respectively, and it can distribute water between turnouts adequately, efficiently, equitably, and dependably in normal conditions.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Amein, M. 1968. “An implicit method for numerical flood routing.” Water Resour. Res. 4 (4): 719–726. https://doi.org/10.1029/WR004i004p00719.
Amein, M., and C. S. Fang. 1970. “Implicit flood routing in natural channels.” J. Hydraul. Div. 96 (12): 2481–2500. https://doi.org/10.1061/JYCEAJ.0002796.
Anwar, A. A., and Z. U. Haq. 2013. “Genetic algorithms for the sequential irrigation scheduling problem.” Irrig. Sci. 31 (4): 815–829. https://doi.org/10.1007/s00271-012-0364-y.
Arauz, T., J. M. Maestre, X. Tian, and G. Guan. 2020. “Design of PI controllers for irrigation canals based on linear matrix inequalities.” Water 12 (3): 855. https://doi.org/10.3390/w12030855.
Babaei, M., A. Roozbahani, and S. M. H. Shahdany. 2018. “Risk assessment of agricultural water conveyance and delivery systems by fuzzy fault tree analysis method.” Water Resour. Manage. 32 (12): 4079–4101. https://doi.org/10.1007/s11269-018-2042-1.
Clemmens, A. J., T. F. Kacerek, B. Grawitz, and W. Schuurmans. 1998. “Test cases for canal control algorithms.” J. Irrig. Drain. Eng. 124 (1): 23–30. https://doi.org/10.1061/(ASCE)0733-9437(1998)124:1(23).
Conde, G., N. Quijano, and C. Ocampo-Martinez. 2021. “Modeling and control in open-channel irrigation systems: A review.” Annu. Rev. Control 51 (Jan): 153–171. https://doi.org/10.1016/j.arcontrol.2021.01.003.
Dehghani, M., H. Riahi-Madvar, F. Hooshyaripor, A. Mosavi, S. Shamshirband, E. K. Zavadskas, and K.-W. Chau. 2019. “Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system.” Energies 12 (2): 289. https://doi.org/10.3390/en12020289.
Fread, D., and T. Harbaugh. 1971. “Open-channel profiles by Newton’s iteration technique.” J. Hydrol. 13 (Jan): 70–80. https://doi.org/10.1016/0022-1694(71)90202-2.
Henderson, F. M. 1966. Open channel flow. New York: Macmillan.
Kanooni, A., and M. J. Monem. 2013. “Integrated stepwise approach for optimal water allocation in irrigation canals.” Irrig. Drain. 63 (1): 12–21. https://doi.org/10.1002/ird.1798.
Kong, L., J. Quan, Q. Yang, P. Song, and J. Zhu. 2019. “Automatic control of the middle route project for South-to-North water transfer based on linear model predictive control algorithm.” Water 11 (9): 1873. https://doi.org/10.3390/w11091873.
Liu, Y., T. Yang, R.-H. Zhao, Y.-B. Li, W.-J. Zhao, and X.-Y. Ma. 2018. “Irrigation canal system delivery scheduling based on a particle swarm optimization algorithm.” Water 10 (9): 1281. https://doi.org/10.3390/w10091281.
Manz, D. H. 1990. “Use of the ICSS model for prediction of conveyance system operational characteristics.” In Proc., Transactions of the 14th Int. Congress on Irrigation and Drainage (ICID), 1–18. Rio de Janerio, Brazil: International Centre for Sport Security.
Menon, J., B. Mudgal, M. Guruprasath, and S. Sivalingam. 2020. “Control of an irrigation branch canal using model predictive controller.” Curr. Sci. 118 (8): 1255. https://doi.org/10.18520/cs/v118/i8/1255-1264.
Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. “Grey wolf optimizer.” Adv. Eng. Software 69 (Mar): 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
Molden, D. J., and T. K. Gates. 1990. “Performance measures for evaluation of irrigation-water-delivery systems.” J. Irrig. Drain. Eng. 116 (6): 804–823. https://doi.org/10.1061/(ASCE)0733-9437(1990)116:6(804).
Monem, M. J., and R. Namdarian. 2005. “Application of simulated annealing (SA) techniques for optimal water distribution in irrigation canals.” Irrig. Drain. 54 (4): 365–373. https://doi.org/10.1002/ird.199.
Nguyen, D., G. Dandy, H. Maier, and J. Ascough. 2016. “Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge.” J. Water Resour. Plann. Manage. 142 (9): 04016025. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000662.
Niu, P., S. Niu, and L. Chang. 2019. “The defect of the Grey Wolf optimization algorithm and its verification method.” Knowl.-Based Syst. 171 (May): 37–43. https://doi.org/10.1016/j.knosys.2019.01.018.
Savari, H., M. Monem, and K. Shahverdi. 2016. “Comparing the performance of FSL and traditional operation methods for on-request water delivery in the aghili network, Iran.” J. Irrig. Drain. Eng. 142 (11): 04016055. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001089.
Shahdany, S. H., S. Taghvaeian, J. Maestre, and A. Firoozfar. 2019. “Developing a centralized automatic control system to increase flexibility of water delivery within predictable and unpredictable irrigation water demands.” Comput. Electron. Agric. 163 (Aug): 104862. https://doi.org/10.1016/j.compag.2019.104862.
Shahdany, S. M. H., A. Firoozfar, J. Maestre, I. Mallakpour, S. Taghvaeian, and P. Karimi. 2018. “Operational performance improvements in irrigation canals to overcome groundwater overexploitation.” Agric. Water Manage. 204 (May): 234–246. https://doi.org/10.1016/j.agwat.2018.04.014.
Shahdany, S. M. H., and A. Roozbahani. 2016. “Selecting an appropriate operational method for main irrigation canals within multicriteria decision-making methods.” J. Irrig. Drain. Eng. 142 (4): 04015064. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000996.
Shahverdi, K., R. Loni, B. Ghobadian, S. Gohari, S. Marofi, and E. Bellos. 2020. “Numerical optimization study of archimedes screw turbine (AST): A case study.” Renewable Energy 145 (Jan): 2130–2143. https://doi.org/10.1016/j.renene.2019.07.124.
Shahverdi, K., and M. J. Monem. 2012. “Construction and evaluation of the bival automatic control system for irrigation canals in a laboratory flume.” Irrig. Drain. 61 (2): 201–207. https://doi.org/10.1002/ird.638.
Shahverdi, K., and M. J. Monem. 2015. “Application of reinforcement learning algorithm for automation of canal structures.” Irrig. Drain. 64 (1): 77–84. https://doi.org/10.1002/ird.1876.
Shahverdi, K., M. J. Monem, and M. Nili. 2015. “Application of reinforcement learning for determining operational pattern of on-request method to deliver and distribute water.” [In Persian.] Iran. Water Soil J. 46 (2): 283–291.
Shahverdi, K., M. J. Monem, and M. Nili. 2016. “Fuzzy SARSA learning of operational instructions to schedule water distribution and delivery.” Irrig. Drain. 65 (3): 276–284. https://doi.org/10.1002/ird.1975.
Strelkoff, T. 1969. “One-dimensional equations of open-channel flow.” J. Hydraul. Div. 95 (3): 861–876. https://doi.org/10.1061/JYCEAJ.0002105.
Sweidan, A. H., N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A.-K. Mohamed. 2016. “Grey wolf optimizer and case-based reasoning model for water quality assessment.” In Proc., 1st Int. Conf. on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt, 229–239. Cham, Switzerland: Springer.
Tikhamarine, Y., D. Souag-Gamane, A. N. Ahmed, O. Kisi, and A. El-Shafie. 2020. “Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm.” J. Hydrol. 582 (Mar): 124435. https://doi.org/10.1016/j.jhydrol.2019.124435.
Yu, S., and H. Lu. 2018. “An integrated model of water resources optimization allocation based on projection pursuit model–Grey wolf optimization method in a transboundary river basin.” J. Hydrol. 559 (Apr): 156–165. https://doi.org/10.1016/j.jhydrol.2018.02.033.
Zheng, Z., Z. Wang, J. Zhao, and H. Zheng. 2019. “Constrained model predictive control algorithm for cascaded irrigation canals.” J. Irrig. Drain. Eng. 145 (6): 04019009. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001390.
Zhong, K., G. Guan, Z. Mao, W. Liao, C. Xiao, and H. Su. 2018. “Linear quadratic optimal controller design for constant downstream water-level PI feedback control of open-canal systems.” In Proc., MATEC Web of Conf., 01056. Les Ulis, France: EDP Sciences.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 148 • Issue 7 • July 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 14, 2020
Accepted: Mar 1, 2022
Published online: Apr 19, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 19, 2022
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