Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA
Yulong Zhang
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
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1,468
400
86
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Cumulative views and downloads
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Total article views: 931 (including HTML, PDF, and XML)
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743
149
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931
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HTML: 743
PDF: 149
XML: 39
Total: 931
Supplement: 57
BibTeX: 45
EndNote: 68
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Total article views: 1,023 (including HTML, PDF, and XML)
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725
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47
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Total: 1,023
Supplement: 106
BibTeX: 25
EndNote: 22
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Total article views: 1,954 (including HTML, PDF, and XML)
Thereof 1,903 with geography defined
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Total article views: 931 (including HTML, PDF, and XML)
Thereof 912 with geography defined
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Total article views: 1,023 (including HTML, PDF, and XML)
Thereof 991 with geography defined
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of...
