An Overview of Machine Learning and Deep Learning Applications in Earth Sciences in 2024: Achievements and Perspectives

Abstract

Machine learning (ML) and deep learning (DL) methods are extensively applied in various fields of Earth sciences, such as oceanography, meteorology, and climatology. These statistical approaches enable efficient processing of large volumes of data, uncovering hidden patterns, reducing or assessing uncertainty in climate and weather forecasts, automating monitoring, and accelerating analytical research. Among most successful examples, one may mention remote sensing data analysis, geophysical processes modeling, approximating unknown physical parameters, and solving statistical weather and climate forecasting problems. However, there are certain challenges, such as the need for large data volumes, computational demands and technical issues of the data science approach, and ensuring the physical plausibility of results. In the future, the development of hybrid models that combine physical and statistical methods is anticipated, as well as improvements in the interpretability of ML and DL models. In this overview, we will examine current achievements in the application of ML and DL in the study of the ocean, atmosphere, and climate, and we will discuss the challenges and prospects for their further development. This overview places particular emphasis on the progress made in the Russian Federation scientific community regarding the application of ML, DL, and AI within Earth sciences, highlighting both its accomplishments and the challenges it faces in the global research landscape.