The optimal operation of hydropower reservoirs is essential for the planning and efficient management of water resources and the production of hydroelectric energy. Various techniques such as the genetic algorithm (GA), artificial neural networks (ANN), support vector machine (SVM), and dynamic programming (DP) have been employed to calculate reservoir operation rules. This paper implements the data mining techniques SVM and ANN to calculate the optimal release rule of hydropower reservoirs under "forecasting" and "non-forecasting" scenarios. The employment of data mining techniques accounting for data uncertainty to calculate optimal hydropower reservoir operation is novel in the field of water resource systems analysis. The optimal operation of the Karoon 3 reservoir, Iran, serves as a test of the proposed methodology. The upstream streamflow, storage records, and several lagged variables are model inputs. Data obtained from solving the reservoir optimization problem with nonlinear programming (NLP) are applied to train (calibrate) the SVM, and ANN, SVM, and ANN are executed in the "non-forecasting" scenario based on all inputs along with their time-lagged variables. In contrast, current parameters are removed from the set of inputs in the "forecasting" scenario. The results of the SVM model are compared with those of the ANN model with the correlation coefficient (R), the mean error (ME), and the root mean square error (RMSE). This paper's results indicate performance of the SVM is better than that of the ANN model by 1.5%, 400%, and 10% with respect to the R, ME, and RMSE diagnostic statistics, respectively. In addition, SVM and ANN overcome data uncertainty ("forecasting" scenario) to produce optimal reservoir operation.
Keywords: Artificial neural network; Hydropower; Real-time reservoir operation; Rule curve; Support vector machine.