Operation of Irrigation Canals using intelligent methods

The rapid growth of population, agriculture, urban and industries has led to increasing water demand and competition for its consumptions. The promotion of agricultural water productivity has the main effect on improving water consumption. Water delivery and scheduling methods are important to increase the flexibility of irrigation systems. Among different available methods, the on-request water delivery has higher flexibility than the rotational one and doesn’t need the high cost of automatic systems. The appropriate adjustment of the structures and their operational instructions between successive requests is a function of discharge variation, time interval between operations, coincidence of different request, physical condition of canal and structures and hydrodynamic behavior of the flow, which is a complex task. To obtain the performance of the recently utilized method, i.e., FSL (Fuzzy SARSA Learning), it is necessary to compare it to a traditional method like ANN (Artificial Neural Network). In this research, the data of the east Aghili canal was trained for programming water delivery and distribution using MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) networks of artificial neural networks with the on-request method. Finally, the results of the FSL and ANN models were compared.

In this research, the MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) networks of artificial neural networks were used to determine the procedure for exploiting the operational instructions of the on-request method in the east Aghili canal, in Khuzestan province, using its flow and gate opening data. In this research, 70%, 15%, and 15% of data were used to train, test, and validate the model, respectively. The correlation coefficient and root mean square error were used for determining the better method. Modeling of the canal was made using the ICSS (Irrigation Canal Conveyance System) hydrodynamic model. To evaluate the MLP, RBF, and FSL outputs, maximum and average errors of water depth, adequacy, efficiency, equity, and dependability were used.

The operational instructions were determined using the MLP for Mars 2017 in the east Aghili canal, and they were compared to the corresponding determined operational instructions using FSL. According to the obtained results, it can be observed that the MPA index in the neural network method in the first and second block of this channel, respectively are 0.952 and 0.919 and in the case of using the FSL method, these values are equal to 0.996 and 1. Also, the MPF index in the simulation using the neural network in both blocks are equal to 1 and in the case of FSL, these values are equal to 0.999 and 0.971. The maximum error of MAE of water level in the first and second block of the study, respectively are equal to 9.2 and 3.8 percent and in the case of using the FSL method, these are equal to 5.5 and 7.4 percent. The results showed that the MLP is better than the RBF to determine the operational instructions. The MAE and IAE indicators were minimum, and the water delivery indicators were close to their desired values according to the Molden and Gates (1990) criteria. Aldo, it was revealed that the FSL is better than the MLP; however, the MLP results are valid and can be used in practice.

In this research, the ANN model was used for determining operational instructions using MATLAB. The training was done using the MLP and RBF using the east Aghili canal data. The ICSS was used for simulating the canal. The results showed that the MLP was better than RBF, and the FSL model was better than the MLP as well. However, both of them can be used in practice.