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Article

Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals

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Department of Water Science Engineering, Bu-Ali Sina University, Hamadan 6517838695, Iran
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Department of Systems and Automation Engineering, University of Seville, 41092 Seville, Spain
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Computer Engineering Department, Faculty of Engineering, Yazd University, Yazd 89195741, Iran
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Department of Water Management, Delft University of Technology, 2611 CD Delft, The Netherlands
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KWR Water Research Institute, 3433 PE Nieuwegein, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2020, 12(9), 2407; https://doi.org/10.3390/w12092407
Received: 24 July 2020 / Revised: 20 August 2020 / Accepted: 23 August 2020 / Published: 27 August 2020
(This article belongs to the Section Water, Agriculture and Aquaculture)
Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action, reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem related to FSL is its convergence and generalization in environments with many variables such as large irrigation canals and situations beyond training. Furthermore, due to its architecture, FSL may require high computation demands during its learning process. To deal with these issues, this work proposes a computationally lighter generalizing learned Q-function (GLQ) model, which benefits from the FSL-learned Q-function, to provide operators with a faster and simpler mechanism to obtain operational instructions. The proposed approach is tested for different water requests in the East Aghili Canal, located in the southwest of Iran. Several performance indicators are used for evaluating the GLQ model results, showing convergence in all the investigated cases and the ability to estimate operational instructions (actions) in situations beyond training, delivering water with high accuracy regarding several performance indicators. Hence, the use of the GLQ model is recommended for determining the operational patterns in irrigation canals. View Full-Text
Keywords: agricultural water management; FSL; GLQ agricultural water management; FSL; GLQ
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MDPI and ACS Style

Shahverdi, K.; Maestre, J.M.; Alamiyan-Harandi, F.; Tian, X. Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water 2020, 12, 2407. https://doi.org/10.3390/w12092407

AMA Style

Shahverdi K, Maestre JM, Alamiyan-Harandi F, Tian X. Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water. 2020; 12(9):2407. https://doi.org/10.3390/w12092407

Chicago/Turabian Style

Shahverdi, Kazem, J. M. Maestre, Farinaz Alamiyan-Harandi, and Xin Tian. 2020. "Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals" Water 12, no. 9: 2407. https://doi.org/10.3390/w12092407

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