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: | , , , , , , , , , , |
|---|---|
| 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!
|
| _version_ | 1832582851391913984 |
|---|---|
| author | Shijie Jiang Lily‐belle Sweet Georgios Blougouras Alexander Brenning Wantong Li Markus Reichstein Joachim Denzler Wei Shangguan Guo Yu Feini Huang Jakob Zscheischler |
| author_facet | Shijie Jiang Lily‐belle Sweet Georgios Blougouras Alexander Brenning Wantong Li Markus Reichstein Joachim Denzler Wei Shangguan Guo Yu Feini Huang Jakob Zscheischler |
| author_sort | Shijie Jiang |
| collection | DOAJ |
| description | 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. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system. |
| format | Article |
| id | doaj-art-eb521b00c73e49509e141f1f17007bc6 |
| institution | Kabale University |
| issn | 2328-4277 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-eb521b00c73e49509e141f1f17007bc62025-01-29T07:58:53ZengWileyEarth's Future2328-42772024-07-01127n/an/a10.1029/2024EF004540How Interpretable Machine Learning Can Benefit Process Understanding in the GeosciencesShijie Jiang0Lily‐belle Sweet1Georgios Blougouras2Alexander Brenning3Wantong Li4Markus Reichstein5Joachim Denzler6Wei Shangguan7Guo Yu8Feini Huang9Jakob Zscheischler10Department of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena GermanyDepartment of Compound Environmental Risks Helmholtz Centre for Environmental Research—UFZ Leipzig GermanyDepartment of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena GermanyELLIS Unit Jena Jena GermanyDepartment of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena GermanyDepartment of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena GermanyELLIS Unit Jena Jena GermanySchool of Atmospheric Sciences Sun Yat–Sen University Zhuhai ChinaDivision of Hydrologic Sciences Desert Research Institute Las Vegas NV USADepartment of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena GermanyDepartment of Compound Environmental Risks Helmholtz Centre for Environmental Research—UFZ Leipzig GermanyAbstract 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. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.https://doi.org/10.1029/2024EF004540machine learningXAIinterpretable machine learningknowledge discoveryinterpretabilitybig data |
| spellingShingle | Shijie Jiang Lily‐belle Sweet Georgios Blougouras Alexander Brenning Wantong Li Markus Reichstein Joachim Denzler Wei Shangguan Guo Yu Feini Huang Jakob Zscheischler How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences Earth's Future machine learning XAI interpretable machine learning knowledge discovery interpretability big data |
| title | How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences |
| title_full | How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences |
| title_fullStr | How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences |
| title_full_unstemmed | How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences |
| title_short | How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences |
| title_sort | how interpretable machine learning can benefit process understanding in the geosciences |
| topic | machine learning XAI interpretable machine learning knowledge discovery interpretability big data |
| url | https://doi.org/10.1029/2024EF004540 |
| work_keys_str_mv | AT shijiejiang howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT lilybellesweet howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT georgiosblougouras howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT alexanderbrenning howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT wantongli howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT markusreichstein howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT joachimdenzler howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT weishangguan howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT guoyu howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT feinihuang howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences AT jakobzscheischler howinterpretablemachinelearningcanbenefitprocessunderstandinginthegeosciences |