Determining operational patterns considering operator’s error in structures settings in irrigation networks

Structures operation in traditional water conveyance and distribution canals is manually done using operators’ experience. Determining operational patterns in these canals is an important issue done in recent studies using artificial intelligence. One of the errors occurring during the settings of the structure is the operators’ error applying some errors as they operate the determined setting. This issue and its effect hasn’t been investigated in the previous research so far. In this research, the reinforcement learning model was used to determine the operational patterns considering the operator errors of five percent and 10 percent applied randomly. A non-linear model of the studied canal that is the E1R1 canal as a part of Dez network located in the north of Khuzestan was employed to simulate. The results showed that reinforcement learning can accurately determine the operational patterns with a maximum iteration of 650 so that the action values are more than 0.9 in most cases.