Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system

This study presents a methodology to establish a set of optimal operation release rules which are tree-based rules for real-time flood control on a multipurpose multireservoir system. The derived rules can be used to determine the optimal real-time releases during flood periods. Steps of the proposed methodology involve: (1) collection of data, (2) building of flood database, (3) generation of optimal input–output patterns by running the flood control optimization model, (4) classification of training and testing data, (5) extraction of tree-based release rules for designed scenarios using the decision-tree algorithm (C5.0), (6) determination of optimal tree-based rules, (7) generation of the real-time forecast data by using the hydrological forecast model, (8) processing of reservoir real-time releases by simulating the reservoir real-time flood control operation, and (9) verification of the superior release rules through comparisons of tree-based rules, regression-based rules derived from a multiple-linear regression model and existing release rules. The developed methodology is applied to the Tanshui River Reservoir System in Taiwan to extract the decision trees for each scenario and then select the best ones with highest accuracy as the optimal tree-based rules. The derived optimal tree-based rules, regression-based rules and existing rules are compared by conducting the real-time operations in three historical typhoons, including Aere, Haima and Nock-ten in 2004. Results demonstrate that the solution using the derived tree-based rules have better performance than the regression-based rules and the existing rules in terms of reducing the peak stage at downstream control points, and meeting the target reservoir storage at the end of flood.