How AI is Shaping the Arctic Research

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How AI is Shaping the Arctic Research

 Due to several factors, the Arctic and polar regions are under constant research and exploration. The Arctic waters and land play a vital role in forecasting and studying the future potential climate changes. Global warming is undoubtedly changing global climate patterns across the globe at an alarming rate. The polar climate is no exception. This change in polar climate, especially in the Arctic region is a major concern for s large group of scientists and researchers around the planet. For decades, different research agencies, both government and privately funded, have been working on measuring the extent of global warming in these regions. However, with the introduction of Artificial Intelligence and Machine Learning, the ongoing research areas have got an immense boost. AI is expected to increase the accuracy and genuineness of polar forecasting. AI will, perhaps, greatly help in oceanic, atmospheric, and ecological observations.   

Before diving further into the impact of AI in polar observations, let's first get an overview of this new technology. The widely and rapidly evolving field of computer science, AI, has successfully marked its impact on a wide spectrum of scientific areas. Researchers across the globe have open-handedly welcomed the involvement of artificial intelligence in their respective research fields. AI models are now an important tool for farming, genetics, astronomy, and several areas of study. While AI models are also a base for the creation of human-like robots, the part of AI which I will be discussing here will be completely different. Here, I will not be mentioning some speaking robots conducting polar studies, but rather computer-based precise scientific and experimental tools used by researchers. AI has a strong application in remote sensing, biomass study, and sound observations in the Arctic.

The newly developed AI tools are helping scientists to forecast and analyze sea ice conditions even more precisely. Now, researchers can easily predict the future conditions of ice sheets. IceNet is the tool developed in 2021 by a group of researchers from the British Antarctic Survey (BAS) and the Alan Turing Institute. IceNet can map ice covers and provide required predictions with up to 95% accuracy. Sea ice is covered across the ocean and is found mostly in polar regions. Earlier, it was quite difficult and inaccurate to predict the state of ice. But, now researchers can predict whether the ice sheet will be present in the upcoming two months or not. These predictions will eventually help to develop early-warning systems to protect coastal establishments and Arctic wildlife. 

The increasing temperature due to global warming has disturbed the sea ice cycle and in the past four decades, the summer sea ice cover of the Arctic is reduced to half of its previous cover. This accelerating change will greatly impact the Arctic ecosystem. Hence, it was necessary to precisely predict future patterns to avoid future circumstances. The typical methods were very slow compared to the IceNet. The tool is successful in providing the need for faster results with better accuracy, up to a certain extent. 

However, predicting the future states of the ice sheets is not the only goal. Now, researchers are working to develop real-time forecasting tools. After successful mapping and prediction, it is necessary to develop the required recovery infrastructures and technology to stop further damage to this ice cover. The AI will surely map the ice cover but will not the further damage. It is up to us, humans, to take the required steps and implements the required measures.        

In addition to the above-mentioned tool, there are various algorithms developed to study the ice cover of polar regions. One such technique is developed by a group of researchers including Maryam Rahnemoonfar and Masoud Yari. They developed a technique to measure ice cover using preexisting data from NASA's Operation Icebridge. However, NASA's method needed significant manual work and also, and the changes made took months, even years, to implement. Even, the data collected from the previous remote sensing techniques required manual work. 

However, the newly developed AI technique can easily work on a huge dataset with better precision. The process of data mining is made easier and faster. This model will help scientists track the thickness of ice sheets and snow accumulation in various locations around the Arctic and even Antarctic regions. The team of Mariam and Yari was not the first to develop such tracking techniques using IceBridge dataset. But, the previous methods were inaccurate and more time-consuming. This new method will now provide accurate measures of ice thickness and will eventually help in measuring sea rise levels. However, the only concern for the trained model is the noise. The optical noises in the images from the dataset lead to several inaccuracies. Significant work is still required on this model to reduce the presence of noise.    

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