Modeling of an Irrigation Main Canal Pool based on a NARX-ANN System Identification

Dynamic models of main irrigation canals are necessary to carry out real-time canal flow control and supervision. Since the hydraulic equations of canal dynamics are nonlinear, their solution involves large computations that impede their use in real-time applications. Then these tasks are often implemented using simple local linear models obtained around an operating point, that are unable to capture the dynamics when large canal operation changes happen. In this paper, we propose a nonlinear dynamic model for main irrigation canal pools based on a new Artificial Neural Network (ANN). This ANN outperforms the above local models because it includes a new input which is the downstream water level of the previous pool. This new architecture with this new input yields models that accurately reproduce the behavior of the canal in a wide range of operations while being simple enough to allow real-time control and supervision. The high accuracy of the proposed model as regards reproducing the observed data is validated for different flow regimes based on experiments developed in a laboratory prototype of the hydraulic canal and in a real main irrigation canal. The contribution of this work is such a general and accurate but simple model of the dynamics of a canal pool. Its novelty is not only being able to reproduce the behavior at different operating points of the canal but also being able to reproduce, in a single model, the free and submerged flow regimes — which are described by dynamic equations with different structures. Moreover, we implement a new classification model to recognize the flow regimes of the irrigation canals using pattern recognition.