Prediction of sea ice evolution in Liaodong Bay based on a back-propagation neural network model

In the present study, a back-propagation neural network model (BP model) was developed with the aim of predicting the sea ice spatial evolution in Liaodong Bay. In addition to air temperature and wind speed, two new variables wind direction and wind duration were used to train the BP model. Validation of the BP model with measurements showed that the BP model can effectively predict the spatial evolution of sea ice. The sensitivity studies indicated that wind direction and wind duration can obviously improve the prediction accuracy of sea ice edge in heavy ice years. Moreover, the BP model was easy to set up as it only used four yearlong periods, 2003-2004, 2005-2006, 2006-2007 and 2009-2010, and the results were not very sensitive to the training dates over the four years. The BP model results were not very sensitive to the training algorithms as well. By comparison with a least-square-based method (LSM), the BP model clearly outperformed the LSM during the period of ice melt with nonlinear characteristics caused by the frequent appearance of cold waves. Furthermore, the BP model had a higher accuracy in estimating the spatial evolution of sea ice compared with a logit model, especially for the ice edge, which is more easily affected by the complex ocean environment.