基于ConvLSTM网络的北极海冰时空序列预测研究OA北大核心CSTPCD
Research on Arctic sea ice spatiotemporal sequence prediction based on convolutional long short term memory network
为了开辟北极航运路线、支持极地科学研究和资源开发,准确预测海冰密集度(SIC)显得尤为关键.在海洋预报领域,统计预报发挥着重要作用.文章引入了一种级联式的卷积长短时记忆神经网络(ConvLSTM)用于北极SIC的中、短期预测.该网络具备图像处理和时空预测的能力,可用于对海冰时空序列进行精确的预报.它能够处理不同长度的输入序列,在各种数据情境下展现出强大的预测潜力.通过对网络架构进行优化,该架构取得了更强的性能,能够更准确地捕捉和分析SIC的动态变…查看全部>>
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 processi…查看全部>>
夏成龙
海军研究院,天津 300061
测绘与仪器
北极海冰人工智能神经网络卷积长短期记忆网络海冰密集度
Arctic sea iceartificial intelligenceneural networksConvLSTMsea ice concentration
《海洋测绘》 2024 (4)
48-53,6
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