Assessing Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model in the Boise River

Using state-of-the-art computational techniques, a genetic algorithm (GA) and an accuracy-based learning classifier system (XCS) were shown to produce optimal operational solutions for gate structures operation in irrigation canals. An XCS has been successfully developed to generate a set of operational rules for canal gates through the exploration and exploitation of rules using a GA, with the support of an unsteady-state hydraulic simulation model. A computer program which implemented the XCS was used to develop operational rules to operate all canal gate structures simultaneously, while maintaining water depth near target values during variable-demand periods, and with a hydraulically stabilized system when demands were no longer changed. Data from two reaches of the Boise River Project were used for assessing performance of the model. In the tested cases, thousands XCS simulations involving thousands of hydraulic simulations, were required to produce satisfactory rules. However, the overall fitness of the set of rules was increased monotonically as XCS simulations progressed. Simulated water depths approached the respective target depths for variable water delivery demand through turnout structures in the simulated canal systems