CURRENT AFFAIRS FOR UPSC IAS

New insights from artificial intelligence about permafrost coverage in the Arctic may soon give policy makers and land managers the high-resolution view they need to predict climate-change-driven threats to infrastructure such as oil pipelines, roads and national security facilities.

Warming Arctic and Rapidly Changing Permafrost

• The Arctic is experiencing warming at a rate four times faster than the global average.
• Permafrost, a vital component of the Arctic, is undergoing rapid changes with significant consequences.

Challenges in Current Models and Infrastructure Threats

• Existing models:Existing models lack the resolution needed to understand the impact of permafrost thaw on the environment and infrastructure.
• Threats:Thawing permafrost poses threats to infrastructure such as oil pipelines, roads, and national security facilities.
• Pan-Arctic models:Current pan-Arctic models have a resolution of about one-third square mile, insufficient for specific location assessments.

Innovative AI Application for Permafrost Data

• Los Alamos National Laboratory:Researchers at Los Alamos National Laboratory, led a team that employed supervised machine learning, a form of AI, for permafrost data analysis.
• High-resolution view:TheAI models offer a high-resolution view, determining permafrost coverage at just under 100 square feet, surpassing the coarse resolution of current pan-Arctic models.
• Higher Accuracy:TheLos Alamos AI model achieved an accuracy of 83% in predicting permafrost coverage, outperforming the 50% accuracy of the pan-Arctic model.

Environmental Impacts and Thawing Permafrost Consequences

• Large Land Extent: Permafrost covers approximately one-sixth of the exposed land in the Northern Hemisphere.
• Environmental hazards:Thawing permafrost leads to environmental hazards, including land-surface subsidence, altered groundwater, changed soil chemistry, and the release of carbon into the atmosphere.
• Climate change impact:Asair temperatures rise due to climate change, thawing ground releases water, impacting lower terrain, rivers, lakes, and oceans.

AI Models Evaluation and Future Plans

• The Los Alamos AI model demonstrated higher accuracy than the pan-Arctic model but requires further improvement for site-specific predictions.
• The team tested three different AI approaches—extremely randomized trees, support vector machines, and an artificial neural network—finding mixed results with support vector machines showing promise for transferability.
• Future plans involve refining AI algorithms for better transferability across diverse Arctic landscapes.

Way Forward:

The integration of AI in permafrost analysis marks a significant advancement, offering a detailed understanding of changes in the Arctic landscape. The technology's potential to provide high-resolution data can play a crucial role in predicting and mitigating climate-change-driven threats to critical infrastructure in the region.