Flexible on‐request (arranged) water delivery is applicable in existing canals that operate manually. A challenge facing this method is the diversity and multiplicity of requests across the network, making planning for water distribution very difficult and time‐consuming. Therefore, we need a suitable tool to derive the proper operation of structures quickly to improve network performance compared to rotational delivery. This study uses intelligent optimization methods to prepare on‐request operational instructions of irrigation canals under unsteady flow conditions. A meta‐model was developed using artificial neural networks (ANNs) for the first reach of the Aghili canal for simulating unsteady flow in canals to speed up the process. The meta‐model was combined with a genetic algorithm (GA) to determine optimal operational instructions. The error rate of the ANN model was smaller than 2.5%, indicating excellent performance of the developed model. Two scenarios of 3‐ and 6‐h delivery were defined to compare the optimal operation of the ANN‐GA model with conventional operation. For both scenarios, the percentage of improvement was remarkable, and on average, it was above 50%. The utility of the developed model for shorter deliveries is significant. Moreover, the model can be generalized to other reaches.
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