Journal Article10.1109/tgrs.2022.3177600
A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season
51
TL;DR: SICNet as mentioned in this paper adopts an encoder-decoder framework with fully convolutional networks (FCNs) and can predict the daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W).
read more
Abstract: This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoder–decoder framework with fully convolutional networks (FCNs) and can predict the SIC (covering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$320\times224$ </tex-math></inline-formula> grids, each with a resolution of 25 km) one-week lead with high accuracy. We design a temporal–spatial attention module (TSAM) to help SICNet capture spatiotemporal dependencies from SIC sequences. The satellite-derived SIC data of 33 years (1988–2020) from the National Snow and Ice Data Center (NSIDC) are employed to train and test the model, 1988–2015 for training, and 2016–2020 for testing. SICNet achieves the mean absolute error (MAE) of 2.67%, the mean absolute percentage error (MAPE) of 8.67%, and the Nash–Sutcliffe efficiency (NSE) of 0.9784 in weekly predicting of SIC during the melting season. SICNet achieves better performance than existing deep-learning-based models. The TSAM reduced the MAE from 2.73% to 2.67%. We evaluate the model’s performance by recursively predicting, from seven- to 28-day leads. We employ the binary accuracy (BACC) metric to measure the accuracy of the predicted sea ice extent (SIE) and compare SICNet with the anomaly persistence (Persist). SICNet shows better performance than Persist with an average BACC on the 28th day of 2016–2019 over 90% (90.17%). For the 28-day lead predictions of three extreme minimum SIE in September 2007, 2012, and 2020, SICNet outperforms Persist with an average improvement of 1.84% in BACC and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16 milkm^{2}$ </tex-math></inline-formula> in the SIE error.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model
TL;DR: In this article , a deep learning-based model is proposed to predict the daily sea ice concentration (SIC) of the Pan-Arctic at a subseasonal scale, which is a challenging task for current studies.
Physical Knowledge-Enhanced Deep Neural Network for Sea Surface Temperature Prediction
TL;DR: In this paper , a combination of an encoder and a generative adversarial network (GAN) is used to capture physical knowledge from the observed data, which can then be used for SST prediction.
17
Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network
TL;DR: In this paper , a lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection.
13
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model
Yunhe Wang,Xiaojun Yuan,Yibin Ren,Mitchell Bushuk,Qihai Shu,Cuihua Li,Xiaofeng Li +6 more
TL;DR: Results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least, and SIPNet can also capture the signal of ENSO and SAM on sea ice.
13
An Internal Waves Data Set From Sentinel‐1 Synthetic Aperture Radar Imagery and Preliminary Detection
TL;DR: In this article , a machine-learning based model is proposed to focus on different channels and the spatial information to better detect IWs in global oceans automatically, and the improved model applied on the IW dataset is up to 98.7%, 96.9% and 98.9%, respectively.
11
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
67K
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
- 06 Sep 2014
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
16.6K
CBAM: Convolutional Block Attention Module
Sanghyun Woo,Jongchan Park,Joon-Young Lee,In So Kweon +3 more
- 08 Sep 2018
TL;DR: Convolutional Block Attention Module (CBAM) as discussed by the authors is a simple yet effective attention module for feed-forward convolutional neural networks, given an intermediate feature map, the module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement.
•Proceedings Article
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu,Vladlen Koltun +1 more
- 30 Apr 2016
TL;DR: This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems.