2021 CSDMS meeting-142
Modeling sea ice motion using various artificial intelligence approaches
Thomas Chen, Academy for Mathematics, Science, and Engineering Rockaway New Jersey, United States. firstname.lastname@example.org
The movement of sea ice is influenced by a number of factors, from winds to ocean currents. As climate change continues to occur rapidly, understanding sea ice drift in the Arctic is a key parameter to understanding the effects of rising temperatures in the region. Recent literature has shown that the Arctic and the Antarctic are most affected by global warming, which raises questions regarding climate justice, as most of the carbon emissions causing anthropogenic climate change are produced in other regions. To analyze this impact, we employ artificial intelligence to predict sea ice drift velocity based on external features. Machine learning is the process of computers gaining insights by seeing and correlating large quantities of data. Using external parameters, including wind speed, and drift velocity ground truth as the inputs of the model, we train multiple different architectures and compare the results. Particularly, we experiment with a convolutional neural network (CNN), a random forest (RF), and a support vector machine (SVM). We also experiment with various model specifications. This research leads to a greater understanding of the Arctic’s response to climate change.