A COMPARISON OF SINGLE-BASIN TANK MODEL AND NEURAL NETWORK

Abstract

Conventionally, physical models such as the tank model have been used as a dam inflow prediction method. Since a physical model can be thought as an approximate function of the actual phenomenon, it should be possible to predict performance equal to or better than a physical model if it is replaced by a neural network. Therefore, in this study, dam inflow predictions of a tank model and a neural network were compared under the conditions that input data were equal.The tank model of this study was able to predict the inflow amount at the time of large-scale flooding with relatively high accuracy by adjusting parameters using the latest observed values. On the other hand, the neural network trained with the same input data showed prediction accuracy equal to or better than that of the tank model. This result suggests that the lower limit of the predictive performance of a neural network is given by physical models.