ALGORITHMS AND METHODS OF CLIMATE CHANGE ASSESSMENT USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES.
Abstract
This paper examines the role and importance of artificial intelligence technologies in climate change assessment. Climate change is a global problem, and accurate information is needed to identify and prevent its effects. It enables fast and efficient analysis of large amounts of data using artificial intelligence, machine learning and deep learning algorithms. The article covers the main techniques used in climate change assessment, including regression models, decision trees, and time series analysis. Ethical and social aspects of artificial intelligence are also discussed. As a result, artificial intelligence technologies are emerging as an important tool in understanding and combating climate change, helping to create a sustainable and safe environment for future generations.
References
1. Hawkins, E., & Sutton, R. (2012). "The potential for seasonal forecasting of climate extremes." Nature Climate Change, 2(9), 641-646.
2. Koster, R. D., et al. (2010). "The Second Phase of the Global Land-Atmosphere Coupling Experiment: A Review of the Results." Journal of Hydrometeorology, 11(5), 1001-1020.
3. Schneider, T., & Held, I. M. (2001). "Discriminating between climate change and natural variability." Geophysical Research Letters, 28(18), 3481-3484.
4. Rummukainen, M. (2010). "State-of-the-art with regional climate models." Wiley Interdisciplinary Reviews: Climate Change, 1(1), 82-96.
5. Friedlingstein, P., et al. (2014). "Current and future global climate impacts resulting from COVID-19." Nature Climate Change, 10(10), 1-7.
