How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
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 tho...
Saved in:
Main Authors: | Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, Alexander Brenning, Wantong Li, Markus Reichstein, Joachim Denzler, Wei Shangguan, Guo Yu, Feini Huang, Jakob Zscheischler |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-07-01
|
Series: | Earth's Future |
Subjects: | |
Online Access: | https://doi.org/10.1029/2024EF004540 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
On the role of knowledge graphs in AI-based scientific discovery
by: Mathieu d’Aquin
Published: (2025-01-01) -
C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
by: Golshid Ranjbaran, et al.
Published: (2025-01-01) -
Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages
by: Tomasz Wiejak, et al.
Published: (2024-12-01) -
Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features
by: Wenjun Zhao, et al.
Published: (2024-12-01) -
Identifying the Roadway Infrastructure Factors Affecting Road Accidents Using Interpretable Machine Learning and Data Augmentation
by: Jonghak Lee, et al.
Published: (2025-01-01)