2021 CSDMS meeting-142: Difference between revisions
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{{CSDMS meeting personal information template-2021 | {{CSDMS meeting personal information template-2021 | ||
|CSDMS meeting first name= | |CSDMS meeting first name=Thomas | ||
|CSDMS meeting last name= | |CSDMS meeting last name=Chen | ||
|CSDMS meeting institute= | |CSDMS meeting institute=Academy for Mathematics, Science, and Engineering | ||
|CSDMS meeting city= | |CSDMS meeting city=Rockaway | ||
|CSDMS meeting country=United States | |CSDMS meeting country=United States | ||
|CSDMS meeting state= | |CSDMS meeting state=New Jersey | ||
|CSDMS meeting email address= | |CSDMS meeting email address=thomasyutaochen@gmail.com | ||
|CSDMS meeting phone=9732958607 | |||
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{{CSDMS meeting select clinics1 2021 | {{CSDMS meeting select clinics1 2021 | ||
|CSDMS_meeting_select_clinics1_2021= | |CSDMS_meeting_select_clinics1_2021=5) Training Datasets for Modeling with AI | ||
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{{CSDMS meeting select clinics2 2021 | {{CSDMS meeting select clinics2 2021 | ||
|CSDMS_meeting_select_clinics2_2021= | |CSDMS_meeting_select_clinics2_2021=1) Introduction to R programming and R applications in landscape ecology | ||
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{{CSDMS meeting select clinics3 2021 | {{CSDMS meeting select clinics3 2021 | ||
|CSDMS_meeting_select_clinics3_2021= | |CSDMS_meeting_select_clinics3_2021=3) Digital Twins in Earth Sciences | ||
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{{CSDMS meeting abstract yes no 2021 | {{CSDMS meeting abstract yes no 2021 | ||
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{{CSDMS meeting abstract title temp2021 | {{CSDMS meeting abstract title temp2021 | ||
|CSDMS meeting abstract title=Modeling | |CSDMS meeting abstract title=Modeling sea ice motion using various artificial intelligence approaches | ||
|Working_group_member_WG_FRG= | |Working_group_member_WG_FRG=Marine Working Group, Hydrology Focus Research Group, Geodynamics Focus Research Group, Ecosystem Dynamics Focus Research Group | ||
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{{CSDMS meeting abstract template 2021 | {{CSDMS meeting abstract template 2021 | ||
|CSDMS meeting abstract=The | |CSDMS meeting abstract=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. | ||
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Latest revision as of 13:40, 17 May 2021
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Modeling sea ice motion using various artificial intelligence approaches
Thomas Chen, Academy for Mathematics, Science, and Engineering Rockaway New Jersey, United States. thomasyutaochen@gmail.com
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.
