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: 1,920 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,444
390
86
1,920
156
69
88
HTML: 1,444
PDF: 390
XML: 86
Total: 1,920
Supplement: 156
BibTeX: 69
EndNote: 88
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads
(calculated since 21 Feb 2023)
Total article views: 901 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
720
142
39
901
54
44
66
HTML: 720
PDF: 142
XML: 39
Total: 901
Supplement: 54
BibTeX: 44
EndNote: 66
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Total article views: 1,019 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
724
248
47
1,019
102
25
22
HTML: 724
PDF: 248
XML: 47
Total: 1,019
Supplement: 102
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: 1,920 (including HTML, PDF, and XML)
Thereof 1,869 with geography defined
and 51 with unknown origin.
Total article views: 901 (including HTML, PDF, and XML)
Thereof 882 with geography defined
and 19 with unknown origin.
Total article views: 1,019 (including HTML, PDF, and XML)
Thereof 987 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...
