[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122848-en":3,"doc-seo-122848-105":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},122848,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","AttentionFire_v1.0 - interpretable machine learning fire model for burned-area predictions over tropics","African and South American wildfires drive over 70% of global burned areas and strongly couple to local climate across sub-seasonal to seasonal dynamics. Yet modeling this wildfire–climate linkage remains difficult because wildfire responses to climate variability and human influence vary across space and time. An interpretable ML fire model (AttentionFire_v1.0) was developed to disentangle these controls and improve burned-area predictions for ASA regions.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nAttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics  \nPermalink  \n[https://escholarship.org/uc/item/5k24d0wj](https://escholarship.org/uc/item/5k24d0wj)  \nJournal  \nGeoscientific Model Development, 16(3)  \nISSN  \n1991-959X  \nAuthors  \nLi, Fa  \nZhu, Qing Riley, William Jet al.  \nPublication Date  \n2023  \nDOI  \n10.5194/gmd-16-869-2023  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nGeosci. Model Dev., 16, 869–884, 2023  \n[https://doi.org/10.5194/gmd-16-869-2023](https://doi.org/10.5194/gmd-16-869-2023)[ ](https://doi.org/10.5194/gmd-16-869-2023)© Author(s) 2023 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nAttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics  \nFa Li 1,2 , Qing Zhu 1 , William J. Riley 1 , Lei Zhao3 , Li Xu4 , Kunxiaojia Yuan 1,2 , Min Chen5 , Huayi Wu2 , Zhipeng Gui6 , Jianya Gong6 , and James T. Randerson4  \n1 Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA  \n2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China  \n3Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA  \n4Department of Earth System Science, University of California Irvine, Irvine, CA, USA  \n5Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA  \n6 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China Correspondence: Qing Zhu ([qzhu@lbl.gov](qzhu@lbl.gov))  \nReceived: 26 July 2022 – Discussion started: 11 August 2022  \nRevised: 20 December 2022 – Accepted: 7 January 2023 – Published: 3 February 2023  \nAbstract. African and South American (ASA) wildfires account for more than 70 % of global burned areas and have strong connection to local climate for sub-seasonalto seasonal wildfire dynamics. However, representation of the wildfire–climate relationship remains challenging due tospatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a highemission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire–climate relationships in historical and future predictions.  \n1 Introduction  \nWildfires modify land surface characteristics, such as vegetation composition, soil carbon, surface runoff, and albedo, with significant consequences for regional carbon, water, and energy cycles (Benavides-Solorio and MacDonald, 2001; Shvetsov et al., 2019; Randerson et al., 2006) . Over African and South American (ASA) regions, where more than 70 % of global burned area occurs, wildfires emit 􀀘 1.4 PgCyr􀀀1 (􀀘 65 % of global wildfire emissions; van Der Werf et al., 2017) and dust and aerosols that can alter regional climate through radiative processes (Etminan et al., 2016;","cbCaialTa3BSsVBC","https://ap.wps.com/l/cbCaialTa3BSsVBC","pdf",4657206,1,17,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does AttentionFire_v1.0 address?\",\"answer\":\"It addresses the challenge of representing the complex relationship between wildfires and climate, including heterogeneous space-time responses and human influences, to improve burned-area prediction over tropical African and South American regions.\"},{\"question\":\"How does AttentionFire_v1.0 perform compared with other ML models?\",\"answer\":\"It substantially improves predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models.\"},{\"question\":\"What key climate mechanism does the model reveal?\",\"answer\":\"The model reveals strong time-lagged control from climate wetness on burned areas.\"}]","AttentionFire_v1.0 - 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