the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TSECfire v1.0: Quantifying Wildfire Drivers and Predictability in Boreal Peatlands Using a Two-Step Error-Correcting Machine Learning Framework
Rongyun Tang
Daniel M. Ricciuto
Anping Chen
Yulong Zhang
Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, a quantitative understanding of natural and anthropogenic influences on the changes in BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2016 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) non-parametric models outperformed parametric models in predicting fire occurrences, and the machine learning model of Random Forest performed the best with the area under the Receiver Operating Characteristic curve ranging from 0.83 to 0.93 across multiple fire data sets, and (3) four sets of factor-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 BP fire prediction and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.
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Rongyun Tang et al.
Status: open (until 18 Apr 2023)
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CEC1: 'Comment on gmd-2023-14', Juan Antonio Añel, 17 Mar 2023
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Dear authors,Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlÂYou have archived your code on GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, please, publish your code in one of the appropriate repositories, and reply to this comment with the relevant information (link and DOI) as soon as possible, as your manuscript should not have been accepted for the Discussions stage because of a lack of compliance with the policy. Also, please, include the relevant primary input/output data.ÂMoreover, you must include in a potentially reviewed version of your manuscript the modified 'Code and Data Availability' section, the DOI of the code (and another DOI for the dataset if necessary).ÂAlso, in the GitHub repository, no license is listed for the code. If you do not include a license, the code remains your property, and nobody can use it. Therefore, when uploading the model's code to the new repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.ÂPlease, reply as soon as possible to this comment with the necessary links and DOIs, so they are available for the peer-review process, as they should be.
Be aware that failure to comply promptly with this request can result in rejecting your manuscript for publication.ÂJuan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/gmd-2023-14-CEC1
Rongyun Tang et al.
Rongyun Tang et al.
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