Resulting from the continuous increase in water demand and uneven water distribution both on time and space, the efforts of pursuing integrated optimal water resource management become critical. In this study, we propose a novel intelligent control methodology that includes the genetic algorithm (GA), fuzzy rule base (FRB), and the adaptive network-based fuzzy inference system (ANFIS) to enhance the efficiency of reservoir operation. The Shihmen reservoir in north Taiwan is used as a case study, and its last thirty-six years hydrological data are used to train and/or verify the models' performance. GA and FRB are used to extract the knowledge based on the historical inflow data with a design objective function and the
traditional rule curve operating strategy, respectively. The ANFIS is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. The practicability and effectiveness of the proposed approach is tested on the operation of the Shihmen reservoir. The results show that the ANFIS models built on different types of knowledge have better performance than the traditional M-5 rule curves in reservoir operation. Moreover, we demonstrate that the ANFIS model can be more intelligent for reservoir operation if more information (or knowledge) is involved.