Application of Controlled Gates Boundary Condition in HEC-RAS in Water Conveyance and Distribution Systems

Document Type : Research Paper

Authors

Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

Abstract

Water level control and regulators have a main role in water conveyance and distribution. Despite the simplicity of structure settings in a steady-state condition, applying an appropriate setting in unsteady flow is complicated. Hence, control logic is used to set these structures, usually developed in languages such as MATLAB, Python, and FORTRAN. To use these logics, they must be combined with hydraulic models. In HEC-RAS, there is an elevation controlled water level boundary condition that can be used to control structures. In this research, the evaluation of the performance of this boundary condition was considered to regulate the water level in the E1R1 canal of the Dez network. The results showed that the rate of opening and closing of the gate has a significant impact on the performance, and if they are chosen correctly, the depth changes will be small. The results showed that the IAE indicator is around 1 percent in all the examined options and except in a few cases where the maximum value of MAE exceeds 10 percent and reaches up to 15 percent, its value is also low. Therefore, it is suggested to use this boundary condition in the control of structures.

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References

  1. Arauz, T., Maestre, J. M., Tian, X., & Guan, G. (2020). Design of PI controllers for irrigation canals based on linear matrix inequalities. Water, 12(3), 855.
  2. Barkhordari, S., & Shahdany, S. M. H. (2021). Developing a smart operating system for fairly distribution of irrigation water, based on social, economic, and environmental considerations. Agricultural Water Management, 250, 106833.
  3. Fatemeh, O., Hesam, G., & Shahverdi, K. (2020). Comparing Fuzzy SARSA Learning (FSL) and Ant Colony Optimization (ACO) Algorithms in Water Delivery Scheduling under Water Shortage Conditions. Irrigation and Drainage Engineering.
  4. Hashemy, S., Monem, M., Maestre, J., & Van Overloop, P. (2013). Application of an In-Line Storage Strategy to Improve the Operational Performance of Main Irrigation Canals Using Model Predictive Control. Journal of Irrigation and Drainage Engineering, 139(8), 635-644.
  5. Hashemy, S., & Van Overloop, P. (2013). Applying decentralized water level difference control for operation of the Dez main canal under water shortage. Journal of irrigation and drainage engineering, 139(12), 1037-1044.
  6. Hernández, J., & Merkley, G. (2011). Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model. I: Design. Journal of irrigation and drainage engineering, 137, 1.
  7. Liu, Y., Yang, T., Zhao, R.-H., Li, Y.-B., Zhao, W.-J., & Ma, X.-Y. (2018). Irrigation Canal System Delivery Scheduling Based on a Particle Swarm Optimization Algorithm. Water, 10(9), 1281.
  8. Lord, S. A., Shahdany, S. M. H., and Roozbahani, A. (2021). Minimization of Operational and Seepage Losses in Agricultural Water Distribution Systems Using the Ant Colony Optimization. Water Resources Management, 3(35) 846-827.
  9. Malaterre, P. O., Rogers, D. C., & Schuurmans, J. (1998). Classification of canal control algorithms. Journal of irrigation and drainage engineering, 124(1), 3-10.
  10. Monem, M. J., & Hoseinzadeh, Z. (2012). Development and Test of a Mathematical Model for Automatic Pivot Weir Based on PID Control. Iranian Journal of Soil and Water Research, 43(1), 87-94.
  11. Shahdany, S. H., Taghvaeian, S., Maestre, J., & Firoozfar, A. (2019). Developing a centralized automatic control system to increase flexibility of water delivery within predictable and unpredictable irrigation water demands. Computers and Electronics in Agriculture, 163, 104862.
  12. Shahverdi, K., & Monem, M. J. (2012). Construction and evaluation of the bival automatic control system for irrigation canals in a laboratory flume. Irrigation and drainage, 61(2), 201-207.
  13. Shahverdi, K., & Monem, M. J. (2015). Application of reinforcement learning algorithm for automation of canal structures. Irrigation and drainage, 64(1), 77-84.
  14. Shahverdi, K., Monem, M. J., & Nili, M. (2016). Fuzzy SARSA learning of operational instructions to schedule water distribution and delivery. Irrigation and Drainage, 65(3), 276-284.
Water and Irrigation Management
Volume 12, Issue 4
January 2023
Pages 847-858

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