Heuristic Input Variable Selection in Multi-Objective Reservoir Operation

Deriving operating rules for multi-objective cascade reservoir systems is an important challenge in water resources management. To address, this study combines a radial basis function network with an evolutionary algorithm to propose a heuristic input variable selection (HIS) method that extracts reservoir operating rules based on feature selection. For a case study of the Hanjiang cascade reservoirs in China, we initially describe the operating rules with radial basis functions and subsequently refine them based on the HIS method. We select the most suitable input variables for each reservoir conditioned on water supply and power generation targets to derive and optimize the rules with a Pareto-archived dynamically dimensioned search algorithm. From this we can analyze input variable selection and the corresponding impact on multi-objective cascade reservoir operations. The results demonstrate that the HIS method selects the input variables accurately and the reservoir operating rules refined by the method could increase water supply by up to 6.6% and power generation by up to 1.2%. The most suitable input variables for reservoir operation vary depending on reservoir objective, however the HIS method appears effective at selecting the appropriate input variables for individual reservoirs in a cascade system.