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
Viewed
Total article views: 1,859 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,403
371
85
1,859
143
67
87
HTML: 1,403
PDF: 371
XML: 85
Total: 1,859
Supplement: 143
BibTeX: 67
EndNote: 87
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Total article views: 847 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
681
128
38
847
46
42
65
HTML: 681
PDF: 128
XML: 38
Total: 847
Supplement: 46
BibTeX: 42
EndNote: 65
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Total article views: 1,012 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
722
243
47
1,012
97
25
22
HTML: 722
PDF: 243
XML: 47
Total: 1,012
Supplement: 97
BibTeX: 25
EndNote: 22
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Viewed (geographical distribution)
Total article views: 1,859 (including HTML, PDF, and XML)
Thereof 1,812 with geography defined
and 47 with unknown origin.
Total article views: 847 (including HTML, PDF, and XML)
Thereof 832 with geography defined
and 15 with unknown origin.
Total article views: 1,012 (including HTML, PDF, and XML)
Thereof 980 with geography defined
and 32 with unknown origin.
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...
