基于 TCN-Attention 模型的多变量黄河径流量预测.

In view of the issue that the runoff change was affected by many factors, characterized by randomness and nonlinearity and was difficult to predict accurately, based on the data of daily average runoff, daily precipitation and daily average sediment concentration of Huayuankou Hydrometrical Station on the Yellow River from 2008 to 2012, a multivariable input TCN Attention model combined with time convolution neural network (TCN) and attention mechanism was proposed to predict the daily runoff of the station, LSTM model and TCN model were selected for prediction and comparison experiments. The results show that the prediction performance of TCN model and TCN Attention model is better than that of LSTM model; The Attention mechanism can improve the prediction performance of TCN model by adjusting the feature vector weight. Compared with TCN model, the MAE, RMSE and MAPE values of TCN Attention model have been decreased by 20.25%, 24.90% and 24.39% respectively; TCN Attention model has better generalization performance, which can improve the prediction level of daily runoff. [ABSTRACT FROM AUTHOR]