Machine learning for Earth System Science (ESS): A survey, statusand future directions for South Asia
Abstract:
This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied. It provides an overview of previous work, ongoing work at the Ministry of Earth Sciences, Gov. of India, and future applications of ML algorithms to some significant earth science problems. We provide a comparison of previous work with this survey, a mind map of multidimensional areas related to machine learning and a Gartner’s hype cycle for machine learning in Earth system science (ESS). We mainly focus on the critical components in Earth Sciences, including atmospheric, Ocean, Seismology, and biosphere, and cover AI/ML applications to statistical downscaling and forecasting problems.
Keywords: Machine learning, Mind map, AI/ML applications in Earth Science. Gartner hype curve
User Documentation
ML For Earth System Science
- Introduction
- Background
- Machine learning algorithms for ESS
- ESS datasets
- Research problems in ESS
- Popular tools to perform machine learning for ESS
- Educational materials to learn machine learning for ESS
- Decision making for machine learning in ESS
- Feature engineering for machine learning in ESS
- Emerging areas in machine learning for ESS
- Applications
- Statistical downscaling
- Seismological events
- Short and medium range forecasting using machine learning
- Machine learning for extended range forecasts
- Machine learning for seasonal and climate scale forecasting
- Machine learning for improving the physical processes in dynamical models
- Machine learning for Nowcasting and tracking the storms cells
- Summary
- References
Datasets
Algorithms
Cloud Notebooks