How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Max Planck Institute for Biogeochemistry · Helmholtz Centre for Environmental Research · +6 more institutions
Abstract
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application.…
Citation impact
- FWCI
- 41.99
- Percentile
- 100%
- References
- 145
Authors
11- SJShijie JiangCorresponding
Max Planck Institute for Biogeochemistry
- LSLily‐belle Sweet
Helmholtz Centre for Environmental Research, Technische Universität Dresden
- GBGeorgios Blougouras
Max Planck Institute for Biogeochemistry, Friedrich Schiller University Jena
- ABAlexander Brenning
Friedrich Schiller University Jena
- WLWantong Li
Max Planck Institute for Biogeochemistry
Topics & keywords
- Process (computing)
- Machine learning
- Computer science
- Artificial intelligence
- Data science