Integration of Automatic Upstream and Downstream Control Systems with Nonlinear Channel Models: Implementation, Running, and Comparison

Document Type : Research Paper

Authors

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

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

10.22059/jwim.2023.358462.1070

Abstract

ICSS model has been used in various researches in irrigation canals. Due to the new capabilities of recent versions of the HEC-RAS model and its advanced strategies, this model has also been considered. The combination of upstream and downstream control systems of classical controller with nonlinear channel simulation models (HEC-RAS and ICSS), the way of implement, and the results comparison are the main objectives of this research. The utilization of the rules strategy in HEC-RAS and comparison of the model is the novelty of this research. For this purpose, a controller has been developed for each regulating structure and an operational program has been developed for each turnout in advanced boundary conditions in the Eastern Dez Canal. The results showed that the HEC-RAS model performed very well and showed a lower error value in the upstream control system than the downstream control, and the maximum MAE and IAE were equal to 5.3% and 1.8%, respectively. Also, the water flow is stable most of the time and there are no fluctuations in the water depth. In the ICSS model, almost similar results were observed, so that the upstream control performs better than the downstream control, but there are more depth changes and instability, and the maximum MAE and IAE were obtained as 9.9% and 0.3%, respectively. In terms of discharge delivery indicators, HEC-RAS outperformed ICSS.

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  1. 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.
  2. Deshays, R., Segovia, P., & Duviella, E. (2021). Design of a MATLAB HEC-RAS Interface to Test Advanced Control Strategies on Water Systems. Water, 13(6), 763.
  3. Fan, Y., Chen, H., Gao, Z., Fan, Y., Chang, X., Yang, M., & Fang, B. (2023). Water distribution and scheduling model of an irrigation canal system. Computers and Electronics in Agriculture, 209, 107866.
  4. 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. 146(9), 1-10.
  5. 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)
  6. Naghaei, R., Monem, M. J., & Hashemy Shahedany, S. M. (2016). Evaluating Various Hydraulic and Operation Conditions of Lopac Gate and Developing its Mathematical Model in Accordance with the ICSS Hydrodynamic Model. Iranian Journal of Irrigation & Drainage, 10(1), 24-35. (In Persian(
  7. Ren, T., Niu, J., Cui, J., Ouyang, Z., & Liu, X. (2021). An application of multi-objective reinforcement learning for efficient model-free control of canals deployed with IoT networks. Journal of Network and Computer Applications, 182, 1030.
  8. Shahverdi, K. (2015). Development of on-request Operation System for Irrigation Networks Using Reinforcement Learning Algorithm (Case Study: East Aghili Calnal). Ph.D., Tarbiat Modares University, Tehran.
  9. 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.
  10. Shahverdi, K., & Monem, M. J. (2015). Application of reinforcement learning algorithm for automation of canal structures. Irrigation and drainage, 64(1), 77-84.
  11. 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.
  12. Shahverdi, K., & Talebmorad, H. (2023). Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient. Water Resources Management, 1-19.