Classifier System for Rule-Based Operation of Canal Gates

Abstract

A classifier system for automatic operation of canal gates was developed and tested through simulation modeling. The classifier system manipulates a population of rules that are trained to “learn” appropriate operational responses to unsteady hydraulic conditions. Each rule has one condition and one associated action. The condition and action pair were applied by matching rules to the current hydraulic status, then taking the gate action specified by the matching rule. Sets of gate operational rules that define appropriate responses to different environment situations, represented by hydraulic transients due to changing water deliveries along the canal, were generated through the classifier system. An apportionment-of-credit algorithm was designed by applying a combination of immediate and delayed rewards. Three components of the delayed reward were used to quantify performance in terms of quick stabilization to target water depths in the canal, and small depth fluctuations. A genetic algorithm was applied to inject new rules. In the best cases, the classifier system produced operational rules that stabilized the simulated canal system within 2% of the target levels in 95% of the simulations. Compared to three local automation methods, the classifier system showed the best overall performance in terms of hydraulic stabilization time and matching target water levels.