An Improved ConvLSTM Network for Arctic Sea Ice Concentration Prediction

He, Jianxin; Zhao, Yong; Yang, Dequan; Zhu, Kexin; Deng, Xiong

doi:10.1109/oceans47191.2022.9977029

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Researchers have used ocean remote sensing data that describe various ocean phenomena as inputs to ConvLSTM models for forecasting. The forecast accuracy of multiple ocean phenomena surpasses that of traditional numerical models, including the forecasting of ocean waves [39], [40], winds, sea surface temperature (SST) [41], and sea ice concentration (SIC) [42], [43]. For instance, Tong et al predicted tropical cyclone intensity and track with ConvLSTM [44], and Petrou et al predicted sea ice movement for a few days ahead with ConvLSTM [45].…”

Section: Ocean Phenomena Forecastingmentioning

confidence: 99%

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

Wang,

2024

IEEE Geosci. Remote Sens. Mag.

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Section: Ocean Phenomena Forecastingmentioning

confidence: 99%

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

Wang,

2024

IEEE Geosci. Remote Sens. Mag.

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“…While the downsampling operation effectively enlarges the receptive field, it may also lead to a loss in resolution, subsequently affecting the accuracy of pixellevel prediction tasks. He et al [28] proposed a multi-layer stacking convolutional long short-term memory network (Multi-Stacking-ConvLSTM) for the daily-scale forecasting of Arctic SIC, with a prediction period of seven days. In this setup, the average RMSE for EOF+LSTM was 18.1%, for CNN+LSTM, it was 16.8%, and the Multi-Stacking-ConvLSTM improved sea ice concentration prediction accuracy to 5.3%.…”

Section: Introductionmentioning

confidence: 99%

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction

Zhang,

Wang,

Huang

et al. 2024

Applied Sciences

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Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM). The model is constructed based on the convolutional long short-term memory network (ConvLSTM) and, for the first time, integrates the encoder–decoder architecture of ConvLSTM (ED-ConvLSTM) with a pyramidal dilated convolution strategy. This approach aims to efficiently capture the spatiotemporal properties of the sea ice concentration and to enhance the identification of its nonlinear relationships. By applying convolutional layers with different dilation rates, the PDED-ConvLSTM model can capture spatial features at multiple scales and increase the receptive field without losing resolution. Further, the integration of the pyramid convolution module significantly enhances the model’s ability to understand complex spatiotemporal relationships, resulting in notable improvements in prediction accuracy and generalization ability. The experimental results show that the sea ice concentration distribution predicted by the PDED-ConvLSTM model is in high agreement with ground-based observations, with the residuals between the predictions and observations maintained within a range from −20% to 20%. PDED-ConvLSTM outperforms other models in terms of prediction performance, reducing the RMSE by 3.6% compared to the traditional ConvLSTM model and also performing well over a five-month prediction period. These experiments demonstrate the potential of PDED-ConvLSTM in predicting Arctic sea ice concentrations, making it a viable tool to meet the requirements for accurate prediction and provide technical support for safe and efficient operations in the Arctic region.

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An Improved ConvLSTM Network for Arctic Sea Ice Concentration Prediction

Cited by 2 publications

References 11 publications

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction

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