基于CNN-BiLSTM的河口区域潮位预测

In the field of ocean science, tidal level prediction is an important research topic with wide application value. With the development of machine learning technology, machine learning methods have gradually become a new approach to tidal level prediction. To further explore the application of machine learning methods in long-term tidal level prediction, this paper constructs a machine learning model based on the Bidirectional Long Short-Term Memory (BiLSTM) network architecture. The hourly tidal level data from a total of 8 stations in the Changjiang Estuary region were input into the model for training, and a convolutional neural network layer was used to extract the periodic information of historical tidal levels over a period of 14 days, resulting in tidal level prediction models for each station. The experimental results show that in the 120-hour prediction task, the average Root Mean Square Error and Mean Absolute Error of the model are both below 20 cm and 17 cm, respectively, with a correlation coefficient higher than 0. 98. The results demonstrate that the improved neural network model is effective in long-term tidal level prediction, significantly improving prediction accuracy and effectively mitigating the deficiencies of existing models in extracting tidal periodic features and addressing cumulative errors in long-term predictions.