%0 Journal Article %1 jiang2024interpretable %A Jiang, Shijie %A Sweet, Lily‐belle %A Blougouras, Georgios %A Brenning, Alexander %A Li, Wantong %A Reichstein, Markus %A Denzler, Joachim %A Shangguan, Wei %A Yu, Guo %A Huang, Feini %A Zscheischler, Jakob %D 2024 %I American Geophysical Union (AGU) %J Earth’s Future %K yaff %N 7 %R 10.1029/2024ef004540 %T How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences %U http://dx.doi.org/10.1029/2024EF004540 %V 12

@article{jiang2024interpretable, added-at = {2025-02-11T16:10:31.000+0100}, author = {Jiang, Shijie and Sweet, Lily‐belle and Blougouras, Georgios and Brenning, Alexander and Li, Wantong and Reichstein, Markus and Denzler, Joachim and Shangguan, Wei and Yu, Guo and Huang, Feini and Zscheischler, Jakob}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/262141f981865a751eaea7d2b60174cdc/scadsfct}, doi = {10.1029/2024ef004540}, interhash = {25f58d5ae6e445e5551e89bcc0258a47}, intrahash = {62141f981865a751eaea7d2b60174cdc}, issn = {2328-4277}, journal = {Earth’s Future}, keywords = {yaff}, month = jul, number = 7, publisher = {American Geophysical Union (AGU)}, timestamp = {2025-02-11T16:10:31.000+0100}, title = {How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences}, url = {http://dx.doi.org/10.1029/2024EF004540}, volume = 12, year = 2024 }