基于 VMD-CNN-LSTM 模型和迁移学习框架的风暴潮预测研究

Aiming at the problems of long time and poor operation of the numerical storm surge prediction model, a combined prediction model of storm surge was constructed based on the idea of " decomposition - prediction-integration", combining variational mode decomposition algorithm (VMD), "point -window" sampling model, convolutional neural network (CNN), long and short-term memory network (LSTM) and numerical integration method. To begin with, VMD is used to decompose the storm surge time series data to obtain multiple components; then, a "poinl -window" sampling model is used to sample the component data and construct the input matrix; subsequently, the input matrix is input to the CNN — LSTM combined prediction framework for prediction; ultimately, the prediction results of each component are integrated to obtain the final storm surge prediction results. The empirical results show that the VMD -CNN - LSTM prediction model has higher prediction accuracy compared with the widely used single model and other combined prediction models. In order to better apply the model to practical engineering, the transfer learning method is used to "transfer" the large data training model to the small data domain, and the results show that the transferred model has better generalization ability even if the amount of new sample data is small.