For years, people have been engaged in various activities that directly or indirectly affect the environment. These activities lead to global warming and climate change. While various studies have been carried out for the solution of these problems for years, studies involving artificial intelligence and machine learning have also started to contribute to the solution. Details are in the rest of our article.


Various studies of human civilization's climate change It reveals that it will be at high risk in the 2050s due to According to the report of the "International Thwaites Glacier Cooperation" organization, consisting of US and British scientists, while the Thwaites glacier in Antarctica is melting rapidly, scientists are trying to understand the impact of climate change on these glaciers using various techniques and models. According to other news, the melting of the Siachen Glacier in the Himalayas has reached alarming levels. It is stated that this situation may completely affect the Indus basin, which includes India and Pakistan. Experts emphasize that time is running out fast and therefore, various solutions must be found as soon as possible to reduce the global threat of climate change.


What Causes Climate Change?


Before we come to a solution, we need to recognize what contributes to the problem of climate change. Starting from the first industrial revolution, some activities carried out by people formed the basis of these problems. Examples of these activities are the destruction of forests, the use of fossil fuels, and the pollution of land and sea. As a result of all these activities, the greenhouse effect was formed and caused global warming. Scientists say that carbon dioxide and other greenhouse gases in the atmosphere continue to increase. Restricting the activities mentioned above can help reduce greenhouse gas emissions to a certain extent.
 

The Role of Artificial Intelligence in Combating Climate Change


In 2020, a study titled "Tackling Climate Change with Machine Learning" was published by the world's most distinguished artificial intelligence experts. This study provides ideas on how artificial intelligence and machine learning can accelerate strategies used to combat climate change. Accordingly, there are different approaches in which artificial intelligence is used to conduct environmental research and to keep climate change under control. These are called rule-based and learning-based approaches. AI studies with rule-based approaches help scientists compile carbon dioxide emission data, while learning-based AI studies interact with problems, diagnose problems, and propose solutions to them.
 

Situations where Artificial Intelligence is Used to Combat Climate Change


The situations in which artificial intelligence is used to control climate change and some related applications are as follows:


 

Global Forest Monitoring


It is an open source web application designed to detect destructed forests in real time using artificial intelligence with satellites. It provides deforestation across the country or in a specific geographic region, bushfire notifications by date and time, data on climate change, and more. In the image below, there is a section about Turkey's forests. You can access the map at global forest watch.

Silvia Terra


This US-based start-up firm provides highly accurate forest-related inventory data. This application, developed by Microsoft, is used to determine the number, size, species and health status of trees in forests using artificial intelligence and satellite imaging. Being aware of the health status of forests, in particular, has an important place in the fight against climate change.

 

Sidewalk Labs


The company uses digital technologies to solve traffic problems in cities. In a project they have realized, they focus on how city traffic flows and enable the control of the points where the traffic is concentrated. The aim of the project is to increase transportation efficiency while reducing air pollution in the city.
 

Cycle GAN


GAN, short for Generative Adversarial Network, was discovered in 2014 by Ian Good fellow, a PhD student at the University of Montreal. This is machine learning technique works with the logic of establishing two neural networks against each other while solving a problem. Cycle GAN, on the other hand, is an application that aims to transfer the characteristic features of one image to another image by using this structure. It acts by training convolutional neural networks (CNN) to perform this transmission. Accordingly, artificial intelligence trains itself to produce images that reveal the situation of geographical regions before and after severe weather conditions. The resulting images are useful to scientists in predicting the effects of various climate change events, and aim to give people an idea of ​​what to do about these situations.