Oct 13, 2021 | Shaoni Ghosh
Climate Change is not just restricted to global warming amplified in the hands of human beings, the latter being responsible for inducing the brutalities towards mother Earth. Another cause is the constant emission of greenhouse gases, mainly carbon dioxide (CO2) and methane (CH4). And all these, in a collective sense, intensify global warming.
The atmospheric C02 levels have been rising at a rapid rate and even, the average sea levels are surging at a rate of about one-eighth of an inch each year.
Artificial Intelligence, with its magical power, contains an inherent potential to participate in cutting down on greenhouse gas emissions and propel towards improving resistance for societies to adapt to climate change.
(Must Check: 8 ways in which AI tackles Climate Change)
According to a new paper published on Monday in Nature Climate Change, it confirms that every region of the plant will be, one day, affected by the rise in temperatures.
The researchers in Germany have employed a machine learning technology, which is a subset of artificial intelligence, to scrutinize more than 60,000 studies based on climate change.The study was assisted by Max Callaghan of Berlin's Mercator Research Institute on Global Commons and Climate Change.
What they have excavated from the study is that 85 percent of the population is affected by climate change, which has been brought by humans. The paper states that the study showcased an overwhelming response; the deadly consequences of climate change have already started affecting "human and natural systems."
And so they deduce that there is a high probability for "attributable anthropogenic impacts" to take place across 80 percent of the world's land area, where 85 percent of the human population inhabit.
As reported by CNET, the study puts forward COP26, the UN Climate Change Conference in Glasgow. This would bring together US President Joe Biden and UK Prime Minister Boris Johnson, but not China's Xi Jinping.
This would enable the greatest leaders to work together towards the reduction in carbon emissions, achieving carbon neutrality.
The researchers generated a machine learning software named as BERT or bidirectional encoder representations from transformers. The required data will be invoked in the algorithm which shall further help in detecting the studies that relate to the consequences of climate change. It doesn't matter even if those studies do not relate with the studies findings.
(Also Check: Other Machine Learning-based Algorithms)
The team concentrated on the study to not just bring the unfortunate situation of the planet in the limelight but to introduce machine learning technology to fill in the spaces that scientific study could not focus on before.
The paper reveals the researchers' aim to plot all such equally significant studies on climate change and debunked the concept of arranging the studies in a chronological order where an observed climate trend and consequences are interrelated and are elaborated with special interest.
Having drawn a thin line between traditional approaches and machine learning approach, the researchers implied that machine learning approach could procure "an expansive preliminary but quantifiably uncertain map."