The history and practice of AI in the environmental sciences
Haupt, S. E., Gagne, D. J., Hsieh, W. W., Krasnopolsky, V., McGovern, A., et al. (2022). The history and practice of AI in the environmental sciences. Bulletin of the American Meteorological Society, doi:https://doi.org/10.1175/BAMS-D-20-0234.1
| Title | The history and practice of AI in the environmental sciences |
|---|---|
| Author(s) | Sue Ellen Haupt, David John Gagne, W. W. Hsieh, V. Krasnopolsky, A. McGovern, C. Marzban, W. Moninger, V. Lakshmanan, P. Tissot, J. K. Williams |
| Abstract | Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an "AI winter" temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the 1980s to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical/dynamical modeling approaches to further advance our science. |
| Publication Title | Bulletin of the American Meteorological Society |
| Publication Date | May 1, 2022 |
| Publisher's Version of Record | https://dx.doi.org/https://doi.org/10.1175/BAMS-D-20-0234.1 |
| OpenSky Citable URL | https://n2t.org/ark:/85065/d73200q5 |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, MILES |