[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127952-en":3,"doc-seo-127952-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127952,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method","More frequent and widespread large fires in the western United States create urgent demand for reliable forecasting methods, especially with longer lead times and high spatial resolution. This research develops an interpretable and accurate hybrid machine learning model that explicitly represents how fuel flammability, fuel availability, and human suppression shape fire outcomes. The model achieves strong predictive performance (F1-score 0.846 ± 0.012), outperforming process-driven fire danger indices and multiple baseline ML models. Using explainable AI, it reveals structural differences among ML models, connects inferred driver–fire relationships to established fire physics, and identifies compound climate controls on large fires and megafires. ","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nProjecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method  \nPermalink  \n[https://escholarship.org/uc/item/3fb0w9gk](https://escholarship.org/uc/item/3fb0w9gk)  \nJournal  \nEarth's Future, 12(10)  \nISSN  \n2328-4277  \nAuthors  \nLi, Fa  \nZhu, Qing Yuan, Kunxiaojia et al.  \nPublication Date  \n2024-10-01  \nDOI  \n10.1029/2024ef004588  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nRESEARCH ARTICLE  \n10.1029/2024EF004588  \nSpecial Collection:  \nAdvancing Interpretable AI/ML Methods for Deeper Insights and Mechanistic Understanding in Earth Sciences: Beyond Predictive Capabilities  \nKey Points:  \n• Substantial structural differences were identified across machine learning  \n(ML) fire models, but existing studies mainly focus on accuracy  \n• We proposed a fire model with higher accuracy and physical interpretability compared to commonly used ML models  \n• Our fire model reasonably reflected actual fire risk and revealed complex climate controls on large fires and megafires in the western US  \nSupporting Information:  \nSupporting Information may be found in the online version of this article.  \nCorrespondence to:  \nF. Li and M. Chen, [fli235@wisc.edu](fli235@wisc.edu); [mchen392@wisc.edu](mchen392@wisc.edu)  \n[Citation:](Citation:)  \nLi, F., Zhu, Q., Yuan, K., Ji, F., Paul, A., Lee, P., et al. (2024). Projecting large fires in the western US with an interpretable and accurate hybrid machine learning method. Earth's Future, 12, e2024EF004588 .  \n[https://doi.org/10.1029/2024EF004588](https://doi.org/10.1029/2024EF004588)  \nReceived 28 FEB 2024 Accepted 24 JUL 2024  \n© 2024 American Family Insurance and The Author(s). Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.  \nThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs  \nLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.  \nProjecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method  \nFa Li1 , Qing Zhu2 , Kunxiaojia Yuan2 , Fujiang Ji1 , Arindam Paul3 , Peng Lee3, Volker C. Radeloff1 , and Min Chen1   \n1Department of Forest and Wildlife Ecology, University of Wisconsin‐Madison, Madison, WI, USA, 2Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, 3American Family Insurance, Madison, WI, USA  \nAbstract More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐ driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were","cbCaihYsXzszDt1s","https://ap.wps.com/l/cbCaihYsXzszDt1s","pdf",2614397,3,1,19,"English","en",105,"# Key Points\n## Model accuracy vs interpretability\n## Physical interpretability via explainable AI\n## Climate controls on large fires and megafires\n# Abstract\n## Study motivation\n## Proposed hybrid ML approach\n## Performance and interpretability findings\n## Implications for climate-driven wildfire risk","[{\"question\":\"What problem does the study address in predicting large fires in the western US?\",\"answer\":\"The study targets the difficulty of making reliable predictions with extended lead times and high spatial resolution, despite large fires becoming more frequent and widespread.\"},{\"question\":\"What is the main contribution of the proposed model?\",\"answer\":\"It is an interpretable and accurate hybrid machine learning model that explicitly represents fuel flammability, fuel availability, and human suppression effects on fires.\"},{\"question\":\"How does the study improve interpretability compared with other ML fire models?\",\"answer\":\"It applies explainable AI techniques to “demystify” each model’s black box, uncovering structural differences and aligning driver–fire relationships with established fire physical principles.\"}]","Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method | 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