Daily runoff prediction method based on long-term and short-term component neural network (LSTCNet) - YAN HUA; ZHANG WENZHAO; ZHANG JINYU (2025)
The invention discloses a daily runoff prediction method based on a long-term and short-term component neural network (LSTCNet) model. The LSTCNet utilizes the advantages of a convolutional layer to extract features, an attention mechanism (AM) is introduced into a long-term component to automatically select relative time in all time steps of long-term data, and a short-term component utilizes a multi-layer residual structure to fuse multi-level features in short-term data, so that the performance of the model is effectively improved. In addition, a traditional AR model is used as a linear neural network part to enhance learning of a linear dependency relationship between variables, and an output predicted value is modified. Experimental results show that the method has good performance in the aspect of daily runoff prediction, long-term and short-term components can play a role in learning respective data, and the strength of an integrated model is displayed. 本发明公布了一种基于长期短期组件神经网络(LSTCNet)模型的日径流预测方法。LSTCNet利用