关键词: 径流预测模型, 时间卷积神经网络, 长短时记忆神经网络, 多源水文数据

Abstract: As traditional methods are difficult to extract multi-source hydrological features and solve the problem of feature redundancy,a monthly runoff prediction model based on improved Temporal-Convolutional-Network (TCN) and Long Short Term Memory (LSTM) was proposed.The model constructed a multi-convolution kernel parallel network to extract multi-source timing features while maintaining the causal convolution characteristics.Dilated convolution was introduced to extract higher order hydrological features,improving the processing efficiency of memory units within a long period.The introduction of residual links enabled the complete features of the bottom stage to be transmitted across stages,which enriched the feature results and optimized the overall network structure,and Lushui River Basin was taken as an example for verification.The experimental results showed that the model was superior to other comparative models in terms of computational efficiency,accuracy and network structure,which verified the effectiveness of the model in hydrological prediction of the basin.

Key words: runoff forecast model, time convolutional neural network, long and short term memory neural network, multi-source hydrological data