PHYSICS-CONSTRAINED DEEP REINFORCEMENT LEARNING FOR ADAPTIVE OPTIMAL CONTROL OF UNSTEADY FLOW IN LARGE IRRIGATION CANALS
Received: 2026-07-15 14:05:56
Published: 2026-04-18
Abstract
Efficient water resource management in large irrigation canals is essential for sustainable agricultural production. However, controlling water flow in long irrigation networks is complicated due to nonlinear hydraulic dynamics, time delays, and external disturbances. This study proposes a physics-constrained deep reinforcement learning framework for adaptive optimal control of unsteady flow in irrigation canals. The proposed method integrates hydraulic knowledge derived from the Saint-Venant equations with reinforcement learning to maintain physically consistent control decisions. A simulation environment representing canal dynamics is used to train an intelligent agent responsible for regulating gate operations. The learning process is constrained by hydraulic equations to ensure realistic and stable system behavior. The results indicate that the proposed approach improves flow regulation accuracy and adaptability under varying operational conditions.
Keywords
List of references
-
Chow V. T. Open-Channel Hydraulics. New York: McGraw-Hill, 1959.
-
Cunge J. A., Holly F. M., Verwey A. Practical Aspects of Computational River Hydraulics. London: Pitman Publishing, 1980.
-
Sutton R. S., Barto A. G. Reinforcement Learning: An Introduction. 2nd ed. Cambridge: MIT Press, 2018.
-
Raissi M., Perdikaris P., Karniadakis G. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, Vol. 378, pp. 686–707.
-
Todini E. A mass conservative and water storage consistent variable parameter Muskingum– Cunge approach. Hydrology and Earth System Sciences, 2007, Vol. 11, pp. 1645–1659.
-
Belleflamme M., Dewals B., Erpicum S., Pirotton M. Automatic control of irrigation canals: A review. Irrigation and Drainage Systems, 2013, Vol. 27, pp. 125–143.
-
Dulac-Arnold G., Evans R., van Hasselt H., et al. Deep reinforcement learning in large discrete action spaces. Proceedings of the AAAI Conference on Artificial Intelligence, 2016.
-
Silver D., Lever G., Heess N., et al. Deterministic policy gradient algorithms. Proceedings of the International Conference on Machine Learning (ICML), 2014.
-
Lillicrap T. P., Hunt J. J., Pritzel A., et al. Continuous control with deep reinforcement learning. International Conference on Learning Representations (ICLR), 2016.
-
Goodfellow I., Bengio Y., Courville A. Deep Learning. Cambridge: MIT Press, 2016.
-
Bertsekas D. P. Dynamic Programming and Optimal Control. Belmont: Athena Scientific, 2012.
-
Gupta H., Shukla S. Artificial intelligence approaches for water resources management: A review. Environmental Modelling & Software, 2020, Vol. 128.
About the Authors
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
