Comparing Fuzzy SARSA Learning and Ant Colony Optimization Algorithms in Water Delivery Scheduling under Water Shortage Conditions
Publication: Journal of Irrigation and Drainage Engineering
Volume 146, Issue 9
Abstract
Water delivery scheduling was investigated in this study using fuzzy state, action, reward, state, action (SARSA) learning (FSL) and ant colony optimization (ACO) methods to find the advantages of a new robust model (FSL) over a conventional model (ACO) in both normal and emergency conditions. The mathematical models of these methods were developed. Three water shortages of 10%, 20%, and 30% were considered in the East Aghili canal, Iran, for the simulation process. Water depth and delivery indicators were used for evaluating the performance of the developed models. The results revealed that the FSL and ACO methods offered almost the same performance for the normal operation condition with high and acceptable indicators. However, the FSL method outperformed the ACO method in terms of performance in three considered emergency operations. It can be concluded that the FSL, as a new method, can schedule water delivery efficiently, adequately, equitably, and dependably. Furthermore, the FSL method is likely to lead to less maximum absolute error (MAE) and integral of absolute magnitude of Error (IAE) in comparison to the ACO method and is therefore recommended.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
References
Anwar, A. A., M. T. Bhatti, and T. T. Devries. 2016. “Canal operations planner. I: Maximizing delivery performance ratio.” J. Irrig. Drain. Eng. 142 (12): 04016057. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001091.
Anwar, A. A., and D. Clarke. 2001. “Irrigation scheduling using mixedinteger linear programming.” J. Irrig. Drain. Eng. 127 (2): 63–69. https://doi.org/10.1061/(ASCE)0733-9437(2001)127:2(63).
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.
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).
Derhami, V., V. J. Majd, and M. N. Ahmadabadi. 2008. “Fuzzy Sarsa learning and the proof of existence of its stationary points.” Asian J. Control 10 (5): 535–549. https://doi.org/10.1002/asjc.54.
Dorigo, M. 1992. “Optimization learning and natural algorithms.” Ph.D. dissertation, Dept. of Artificial Intelligence, Politecnico di Milano.
Dorigo, M., and L. M. Gambardella. 1997. “Ant colony system: A cooperative learning approach to the traveling salesman problem.” IEEE Trans. Evol. Comput. 1 (1): 53–66. https://doi.org/10.1109/4235.585892.
Dorigo, M., V. Maniezzo, and A. Colorni. 1996. “Ant system: Optimization by a colony of cooperating agents.” IEEE Trans. Syst. 26 (1): 29–41. https://doi.org/10.1109/3477.484436.
Elghandour, H. A., and E. Elbeltagi. 2018. “Comparison of five evolutionary algorithms for optimization of water distribution networks.” J. Comput. Civ. Eng. 32 (1): 04017066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000717.
Ghodousi, H. 2007. “Comprehensive classification of unsteady flow from operational point of view in irrigation canals and developing mathematical models for their optimal management.” [In Persian.] Ph.D. dissertation, Dept. of Water Structure Engineering, Tarbiat Modares Univ.
Jalali, M. R., A. Afshar, and M. A. Marino. 2005. “Ant colony optimization algorithm (ACO): A new heuristic approach for engineering optimization.” WSEAS Trans. Inf. Sci. Appl. 2 (5): 606–610.
Jalali, M. R., A. Afshar, and M. A. Marino. 2006. “Improved ant colony optimization algorithm for reservoir operation.” Sci. Iranica 13 (3): 295–302.
Katiyar, S., I. Nasiruddin, and A. Q. Ansari. 2015. “Ant colony optimization: A tutorial review.” In Proc., National Conf. on Advances in Power and Control. Faridabad, Haryana: Manav Rachna International Univ.
Korkmaz, N., M. Avci, H. B. Unal, S. Asik, and M. Gunduz. 2009. “Evaluation of the water delivery performance of the Menemen Left Bank irrigation system using variables measured on-site.” J. Irrig. Drain. Eng. 135 (5): 633–642. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000053.
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 (PSO) algorithm.” Water 10 (9): 1281. https://doi.org/10.3390/w10091281.
Lopez-Ibanez, M., T. D. Prasad, and B. Paechter. 2008. “Ant colony optimization for the optimal control of pumps in water distribution networks.” J. Water Resour. Plann. Manage. 34 (4): 337–346. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:4(337).
Manz, D. H. 1990a. “Systems analysis of irrigation conveyance systems.” In Proc., Int. Symp. on Water Resource Systems. Winnipeg, MB: Dept. of Civil Engineering, Univ. of Manitoba.
Manz, D. H. 1990b. “Use of the ICSS model for prediction of conveyance system operational characteristics.” In Proc., 14th Int. Congress on Irrigation and Drainage. New Delhi, India: International Commission on Irrigation and Drainage.
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.
Monem, M. J., and M. A. Nouri. 2010. “Application of PSO method for optimal water delivery in irrigation networks.” [In Persian.] Iran. J. Irrig. Drain. 4 (1): 73–82.
Nguyen, D. C. H., G. C. Dandy, H. R. Maier, and J. C. 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.
Savari, H., M. J. 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.
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.
Sugeno, M., and T. Takagi. 1983. “Multi-dimensional fuzzy reasoning.” Fuzzy Sets Syst. 9 (1): 313–325. https://doi.org/10.1016/S0165-0114(83)80030-X.
Information & Authors
Information
Published In
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jun 1, 2019
Accepted: Apr 22, 2020
Published online: Jul 6, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 6, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.