Air entrainment is an effective approach to protect release works from cavitation damage. The traditional method of aera-tor device designs is that, for given flow conditions, the geometries of the aerator device are designed and then the effects are experi-mentally tested for cavitation damage control. The present paper proposes an inverse problem method of determining the bottom slopes in front of and behind an aerator if the requirements of air entrainment, flow conditions and some of aerator geometric para-meters are given. An RBF neural network model is developed and the relevant bottom slopes are calculated in different conditions of flow and geometry on the basis of the data of 19 aerator devices from different discharge tunnels with safe operation. The case study shows that the methodology provides an effective way to design aerator devices under given target conditions.