Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction

• Published in: Remote Sensing
• Main Authors: Junhwa Chi, Jihyun Bae, Young-Joo Kwon
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: Arctic sea ice; convolutional neural network; long- and short-term memory; visual geometry group (VGG); loss function; deep learning; Science; Q; Arctic; Global warming; Sea ice
• Online Access: https://doi.org/10.3390/rs13173413; https://doaj.org/article/43272f774c2d45918f18dc057d75fcb5

Arctic sea ice plays a significant role in climate systems, and its prediction is important for coping with global warming. Artificial intelligence (AI) has gained recent attention in various disciplines with the increasing use of big data. In recent years, the use of AI-based sea ice prediction, along with conventional prediction models, has drawn attention. This study proposes a new deep learning (DL)-based Arctic sea ice prediction model with a new perceptual loss function to improve both statistical and visual accuracy. The proposed DL model learned spatiotemporal characteristics of Arctic sea ice for sequence-to-sequence predictions. The convolutional neural network-based perceptual loss function successfully captured unique sea ice patterns, and the widely used loss functions could not use various feature maps. Furthermore, the input variables that are essential to accurately predict Arctic sea ice using various combinations of input variables were identified. The proposed approaches produced statistical outcomes with better accuracy and qualitative agreements with the observed data.