[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125793-en":3,"doc-seo-125793-105":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},125793,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0","Wildfires increasingly threaten boreal peatland (BP) ecosystem sustainability and can destabilize boreal carbon storage. Forecasting rare and extreme BP fire occurrence remains difficult, and the quantitative contribution of both natural and human drivers is still unclear. This study quantifies BP fire predictability and key controlling factors from 1997 to 2015 using a two-step error-correcting machine learning framework combining multiple classifiers, regression, and error-correction. Results show oversampling improves recall, nonparametric models—especially random forest—perform best, and simulations indicate temperature, air dryness, and climate extremes dominate over precipitation, wind, and human activities.","Geosci. Model Dev., 17, 1525–1542, 2024 [https://doi.org/10.5194/gmd-17-1525-2024](https://doi.org/10.5194/gmd-17-1525-2024)[ ](https://doi.org/10.5194/gmd-17-1525-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nQuantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0  \nRongyun Tang 1,2 , Mingzhou Jin 1 , Jiafu Mao3 , Daniel M. Ricciuto3 , Anping Chen4 , and Yulong Zhang 1  \n1Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA  \n2Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA  \n3Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA  \n4Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA Correspondence: Mingzhou Jin ([jin@utk.edu](jin@utk.edu)) and Jiafu Mao ([maoj@ornl.gov](maoj@ornl.gov))  \nReceived: 29 January 2023 – Discussion started: 21 February 2023  \nRevised: 6 November 2023 – Accepted: 7 November 2023 – Published: 21 February 2024  \nAbstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets offactor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and dis-  \nentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.  \n1 Introduction  \nCarbon-rich boreal peatlands (BPs) cover only 􀀘 2 % of the Earth's surface (Gorham, 1991) but have accumulated 􀀘 20 %–40 %(450 􀀆 150 PgC) of the global soil carbon, historically having a net cooling effect on the global radiation balance (Hugelius et al., 2020; Page and Hooijer, 2016; Scharlemann et al., 2014) . This major land carbon pool, however, is highly vulnerable to current global warming, which tends to introduce carbon emissions into the atmosphere through increasing decomposition of peat soil organic matter and fire combustion (Turetsky et al., 2014) . In particular, BP fire regimes have been undergoing pronounced changes over recent decades in terms of fire extent, frequency, and duration (Field and Raupach, 2004; Kelly et al., 2013) . In BPs, there are two types of wildfires – surface flaming and underground smoldering – that can transition from one to the other at different phases. It is noteworthy that compared to flaming combustion, smoldering combustion is easier to ignite, harder to suppress","cbCainI41LhEELiG","https://ap.wps.com/l/cbCainI41LhEELiG","pdf",2519481,1,18,"English","en",105,"# Abstract\n# Introduction\n## Boreal peatlands and fire relevance\n## Smoldering combustion and knowledge gaps\n## Drivers and modeling challenges","[{\"question\":\"Why are boreal peatland fires difficult to predict quantitatively?\",\"answer\":\"Rare and extreme BP fire occurrence is challenging to forecast, and the quantitative roles of both natural and anthropogenic drivers have remained elusive.\"},{\"question\":\"What does the two-step error-correcting machine learning framework do in this study?\",\"answer\":\"It combines multiple machine learning classifiers and regression models with an error-correcting technique to address unbalanced data and to reduce overestimation of fire sizes during fire seasons.\"},{\"question\":\"Which factors are identified as dominant drivers of boreal peatland fires?\",\"answer\":\"Factor-control simulations consistently highlight temperature, air dryness, and climate extremes (e.g., frost) as dominant drivers, overriding precipitation, wind speed, and human activities.\"}]","Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 | 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