University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 56 5 2025 07 23 Development of an Inverse Reinforcement Learning Method Integrated with the ICSS Hydrodynamic Model in the Dez canal Development of an Inverse Reinforcement Learning Method Integrated with the ICSS Hydrodynamic Model in the Dez canal 1339 1353 103190 10.22059/ijswr.2025.392613.669911 FA Amirhossein Tavana Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran. کاظم Shahverdi Water Sciences and Engineering Department, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran. Hesam Ghodousi Department of Water Engineering, Faculty of Agriculture, University of Zanjan , Zanjan, Iran Journal Article 2025 03 28   Optimal water resource management in irrigation networks has been recognized as a key strategy to address water scarcity challenges. This research developed a novel hybrid method based on inverse reinforcement learning (IRL) and hydrodynamic modeling with ICSS to optimize the operation of the E1-R1 canal in the Dez network. In this method, the reward function was automatically extracted from the experiences of expert operators (simulated using ICSS), and then, by combining it with the hydrodynamic simulation model, optimal control policies for water control structures and intakes were determined. Operational data under various scenarios, including random inflow rates (1.1 to 3.1 cubic meters per second) and different water withdrawal patterns, were analyzed. The results indicated high efficiency of the proposed system, with an average water delivery efficiency of 0.97, a supply adequacy of 0.95, and a maximum depth control error of 14.3%. The cumulative depth error also remained below 10% in all scenarios, indicating long-term system stability. These findings confirm that the IRL approach, by learning the implicit knowledge of operators and converting it into policies, can effectively reduce water losses and improve the performance of irrigation networks.   Optimal water resource management in irrigation networks has been recognized as a key strategy to address water scarcity challenges. This research developed a novel hybrid method based on inverse reinforcement learning (IRL) and hydrodynamic modeling with ICSS to optimize the operation of the E1-R1 canal in the Dez network. In this method, the reward function was automatically extracted from the experiences of expert operators (simulated using ICSS), and then, by combining it with the hydrodynamic simulation model, optimal control policies for water control structures and intakes were determined. Operational data under various scenarios, including random inflow rates (1.1 to 3.1 cubic meters per second) and different water withdrawal patterns, were analyzed. The results indicated high efficiency of the proposed system, with an average water delivery efficiency of 0.97, a supply adequacy of 0.95, and a maximum depth control error of 14.3%. The cumulative depth error also remained below 10% in all scenarios, indicating long-term system stability. These findings confirm that the IRL approach, by learning the implicit knowledge of operators and converting it into policies, can effectively reduce water losses and improve the performance of irrigation networks. https://ijswr.ut.ac.ir/article_103190_b7b65ada689221232f1e84d3058c4345.pdf