基于改进注意力机制的LSTM 水位预测模型研究

为了进一步提高水位预测的准确性，本文提出一种融入改进注意力机制的长短期记忆网络（Long Short Time Memory,LSTM）预测模型。该模型将输入序列拆分为时间序列和特征序列，在LSTM网络模型前引入注意力机制对两个序列分别进行注意力计算，然后再进行融合，LSTM网络能够根据重要程度自适应地选择最重要的输入特征，注意力机制层的参数通过竞争随机搜索算法获取，从而进一步增强了模型的鲁棒性。最后在鄱阳湖的水位数据上进行预测实验，结果表明：相对于支持向量回归（SVR）、LSTM等模型，本文提出基于改进注意力机制的LSTM模型具有更好的预测精度，可为水位预测和水资源的精准调度提供技术支持。

Study on the LSTM model for water level prediction based on the improved attention mechanism

In order to further improve the accuracy of water level prediction, this paper proposes a Long Short Time Memory (LSTM) prediction model integrated with improved attention mechanism. The model divides the input sequence into the time sequence and the characteristic sequence. The attention mechanism is introduced before the LSTM network model to compute the attention of these two sequences separately. After intergrating, the LSTM network is able to adaptively select the most important input features according to their importance, and the parameters of the attention mechanism layer are obtained by a competitive random search algorithm.Thus, the robustness of the model is further enhanced. Finally, a prediction experiment is conducted on the water level data of Poyang Lake. The experimental results show that compared with the support vector regression (SVR) and LSTM model, the proposed LSTM model based on improved attention mechanism has better prediction accuracy and can provide technical support for water level prediction and precise regulation of water resources.