Research on Arctic sea ice spatiotemporal sequence prediction based on convolutional long short term memory network
Accurate prediction of sea ice concentration(SIC)is particularly crucial for opening Arctic shipping routes,supporting polar scientific research and resource development.In the field of ocean forecasting,statistical forecasting plays a vital role.This study introduces a cascaded Convolutional Long Short-Term Memory neural network(ConvLSTM)for medium-and short-term prediction of Arctic Sea ice concentration.This network has the ability of image processing and spatiotemporal prediction,which can accurately predict the spatiotemporal sequences of sea ice.It can handle input sequences of different lengths and demonstrates strong predictive potential in various data contexts.By optimizing the network architecture,it achieves stronger performance,accurately capturing and analyzing dynamic changes in SIC.Experimental results show that this model achieves a root mean square error of 0.0599 and a correlation coefficient of 95.42%in a 7-day forecast.
国家科技期刊平台
NSTL
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