[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123962-en":3,"doc-seo-123962-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123962,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences - Research Article","Interpretable Machine Learning (IML) is positioned as a new way to advance understanding of the complex Earth system by explaining the reasoning behind model predictions rather than only producing outputs. The study argues that geoscientific impact has not been fully recognized and that fast adoption has sometimes involved careless use. It identifies practical application scenarios, proposes a general workflow, and highlights common pitfalls to ensure reliable, robust insights. The work aims to integrate IML thoughtfully as an essential Earth-science data science tool.","RESEARCH ARTICLE  \n10.1029/2024EF004540  \nSpecial Collection:  \nAdvancing Interpretable AI/ML Methods for Deeper Insights and Mechanistic Understanding in Earth Sciences: Beyond Predictive Capabilities  \nKey Points:  \n• We demonstrate the broader relevance of Interpretable Machine Learning  \n(IML) to most geoscientists and underexplored opportunities for its use  \n• We describe a workflow for the effective use of IML while cautioning against potential and common pitfalls  \n• We suggest good practices for its adoption and advocate for more careful application to ensure reliable and robust insights for the field  \nSupporting Information:  \nSupporting Information may be found in the online version of this article.  \nCorrespondence to:  \nS. Jiang,  \n[sjiang@bgc-jena.mpg.de](sjiang@bgc-jena.mpg.de)  \nCitation:  \nJiang, S., Sweet, L.‐b., Blougouras, G., Brenning, A., Li, W., Reichstein, M., et al.(2024) . How interpretable machine learning can benefit process understanding in the geosciences. Earth's Future, 12, e2024EF004540. [https://doi.org/10.1029/](https://doi.org/10.1029/)[ ](https://doi.org/10.1029/)2024EF004540  \nReceived 8 FEB 2024 Accepted 14 JUN 2024  \nAuthor Contributions:  \nConceptualization: Shijie Jiang, Lily‐ belle Sweet, Georgios Blougouras, Wantong Li, Markus Reichstein, Wei Shangguan, Guo Yu,  \nJakob Zscheischler  \nFormal analysis: Markus Reichstein  \nFunding acquisition: Shijie Jiang, Markus Reichstein  \n© 2024. The Author(s) .  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nHow Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences  \nShijie Jiang1,2 , Lily‐belle Sweet3,4 , Georgios Blougouras1,2,5 , Alexander Brenning2,5 , Wantong Li1 , Markus Reichstein1,2 , Joachim Denzler2,6, Wei Shangguan7 , Guo Yu8 , Feini Huang1,2,7 , and Jakob Zscheischler3,4,9   \n1Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany, 2ELLIS Unit Jena, Jena, Germany, 3Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 4Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, Germany, 5Department of Geography, Friedrich Schiller University Jena, Jena, Germany, 6Computer Vision Group, Friedrich Schiller University Jena, Jena, Germany, 7School of Atmospheric Sciences, Sun Yat–Sen University, Zhuhai, China, 8Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV, USA, 9Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany  \nAbstract 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","cbCailKc9kzhw7k0","https://ap.wps.com/l/cbCailKc9kzhw7k0","pdf",1788391,1,20,"English","en",105,"# 1. Introduction\n# Key Points and Contributions\n## Practical Application Scenarios\n## Workflow and Good Practices\n## Common Pitfalls","[{\"question\":\"What is interpretable machine learning (IML) in the context of this article?\",\"answer\":\"IML is described as going beyond conventional machine learning by seeking to clarify the reasoning behind predictions. This combination of prediction and transparency is used to uncover relationships in Earth-science data.\"},{\"question\":\"How does the paper propose using IML to support geoscientists’ process understanding?\",\"answer\":\"The paper focuses on pragmatic use cases such as quantifying relationships, generating hypotheses about mechanisms, and evaluating process-based models. It also provides a general, practical workflow to address specific research questions.\"},{\"question\":\"What are the main risks of IML adoption discussed in the article?\",\"answer\":\"The article emphasizes that careless or uncritical application can produce misleading conclusions. It identifies several critical and common pitfalls and proposes good practices to avoid them.\"}]","How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences - Research Article | PDF",1785819468,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-interpretable-machine-learning-can-benefit-process-understanding-in-the-geosciences-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/how-interpretable-machine-learning-can-benefit-process-understanding-in-the-geosciences-research-article/123962/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is interpretable machine learning (IML) in the context of this article?","Question",{"text":75,"@type":76},"IML is described as going beyond conventional machine learning by seeking to clarify the reasoning behind predictions. This combination of prediction and transparency is used to uncover relationships in Earth-science data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper propose using IML to support geoscientists’ process understanding?",{"text":80,"@type":76},"The paper focuses on pragmatic use cases such as quantifying relationships, generating hypotheses about mechanisms, and evaluating process-based models. It also provides a general, practical workflow to address specific research questions.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main risks of IML adoption discussed in the article?",{"text":84,"@type":76},"The article emphasizes that careless or uncritical application can produce misleading conclusions. 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