Check Structure Level Regulation in Water Supply Canals using RBO in HEC-RAS

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.

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

10.22059/jwim.2023.356700.1062

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 one 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.

Keywords

Main Subjects


  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. Bonet, E., Gómez, M., Yubero, M., & Fernández-Francos, J. (2017). GOROSOBO: an overall control diagram to improve the efficiency of water transport systems in real time. Journal of Hydroinformatics, 19(3), 364-384.
  3. Clemmens, A. J., Kacerek, T. F., Grawitz, B., & Schuurmans, W. (1998). Test cases for canal control algorithms. Journal of Irrigation and Drainage Engineering, 124(1), 23-30.
  4. Daneshfaraz, R., Dasineh, M., & Ghaderi, A. (2019). Evaluation of Scour Depth around Bridge Piers with HEC-RAS (Case study: Bridge of Simineh Rood, Miandoab, Iran). Environment and Water Engineering ,5(2), 91-102.
  5. Figueiredo, J., Botto, M. A., & Rijo, M. (2013). SCADA system with predictive controller applied to irrigation canals. Control Engineering Practice, 21(6), 870-886.
  6. 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.
  7. 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.
  8. Herrera, J., Ibeas, A., & de la Sen, M. (2013). Identification and control of integrative MIMO systems using pattern search algorithms: An application to irrigation channels. Engineering Applications of Artificial Intelligence, 26(1), 334-346.
  9. Mollazeynali, H., & shahverdi, k. (2022). Application and Evaluation of Elevation Controlled Gates Boundary Condition in HEC-RAS in Water Conveyance and Distribution Systems. Water and Irrigation Management,12(4), 847-858.(In Persian)
  10. 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.
  11. Shahverdi, K., & Monem, M. J. (2015). Application of reinforcement learning algorithm for automation of canal structures. Irrigation and drainage, 64(1), 77-84.
  12. 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.
  13. Tavares, I., Borges, J., Mendes, M. J., & Botto, M. A. (2013). Assessment of data-driven modeling strategies for water delivery canals. Neural Computing and Applications, 23(3), 625-633.
  14. van Overloop, P.-J., Horváth, K., & Aydin, B. E. (2014). Model predictive control based on an integrator resonance model applied to an open water channel. Control Engineering Practice, 27, 54-60.