It’s quite ironic when you think how the most intelligent species considered in the universe is silently destroying its only home. We are on the verge of a mass global climatic shift that has already begun and is causing disruptions in several habitats across the world.
And no matter how much we try to deny or escape the situation, there is concrete scientific evidence giving us a reality check from time to time. According to recent research by NASA, one of the leading causes of climate change is the emission of greenhouse gases, ultimately leading to the phenomenon of global warming. When you try to discover the nuances, it all comes down to the fact that there is a 95 percent probability that it is nothing else but the human actions in the past 50 years that have warmed the planet.
While this is just one example of how human actions have led to drastic consequences for the planet, the list seems endless. Even though the leaders across the world realize the urgency of the issue, the pace of actual action is quite slow. But, thanks to the progress in the field of science and technology, our world is far more advanced than it was ever before.
Today, we have technologies like artificial intelligence and machine learning that are transforming different industries they penetrate. And scientists have also found ways to harness their potential for climate change issues. Let’s take a look at the top ML algorithms that are being used to tackle the quintessential climate change crisis-
When it comes to climate change, one of the most fundamental tasks that a machine learning algorithm can do is trying to learn from the existing data that is available and make predictions on the top of it.
On the one hand, this helps derive meaningful insights from the abundant data we have, something that is hard to accomplish by humans in a limited time. While on the other hand, it gives us a peek into the future of the existing crisis. ML models generated for predictive analysis include regression analysis, naive Bayes classifier, and more.
Yet another class of machine learning algorithms is the general adversarial networks. These networks are gaining popularity these days due to statistical and new information generative capabilities. For example, GANs can be popularly used to generate images of a specific area before and after a weather event takes place. It gives insight into the vivid and personalized outcomes of climate change, where one network learns the properties of the dataset and the other tries to spot the fake.
Neural networks, along with their deeper models known as DNNs, can be distinctively used for time series analysis along with detection and classification tasks in climate change studies. DNNs can outperform several other algorithms because of their capability to act on a limited number of data. Statistics suggest that the accuracy of the best DNN global model in the convolution neural networks is found to be around 97 percent using LeNET.
Parametric Estimation Methods and BIM
The existing Building Information Models that have existed for several decades are now being combined with parametric estimation algorithms to design energy-efficient and sustainable architecture. This practice allows every variable to be manipulated so that the outcomes of an equation can be altered.
Climate change is an existential crisis, and the sooner we realize it, the better strategies we will develop to combat it. While technology is playing a huge role in opening our eyes, it is also the means to bring about radical transformations, starting right now.