Renmin Zhujiang (Jan 2022)
Application of NARX in Seepage Prediction of Earth-Rockfill Dams
Abstract
Seepage monitoring is an important means to master the safety state of a dam.As the seepage pressure of earth-rockfill dams lags behind the reservoir water level,a nonlinear auto-regressive neural network with exogenous inputs (NARX),a network characterized by delayed input,is introduced to effectively predict the seepage pressure of the dams.In the case of a reservoir dam,with the influencing factors such as the reservoir water level and rainfall in a certain period of history as the input sequence and the measured value of seepage pressure as the output sequence,the multi-factor and single-factor models of the NARX network are built separately for fitting training and multi-step prediction.Then,the prediction results are compared with those of the traditional regression model and the traditional BP neural network.The results reveal that the NARX model outperforms the two traditional models under the three accuracy indexes of RMSE,MAE,and MAPE.Moreover,the NARX model still has good performance under the condition of a single factor.The delayed input characteristic of the NARX network can simulate the hysteresis of dam seepage to a certain extent,and the network has a good application effect for seepage pressure prediction of earth-rockfill dams.
