Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographic Sciences, Hebei Normal University, Shijiazhuang, 050024, China
Xi Chen
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station, Tianjin University, Tianjin, 300072, China
Viewed
Total article views: 4,403 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
3,693
614
96
4,403
186
103
166
HTML: 3,693
PDF: 614
XML: 96
Total: 4,403
Supplement: 186
BibTeX: 103
EndNote: 166
Views and downloads (calculated since 05 Jan 2023)
Cumulative views and downloads
(calculated since 05 Jan 2023)
Total article views: 3,400 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,920
402
78
3,400
125
95
153
HTML: 2,920
PDF: 402
XML: 78
Total: 3,400
Supplement: 125
BibTeX: 95
EndNote: 153
Views and downloads (calculated since 12 Oct 2023)
Cumulative views and downloads
(calculated since 12 Oct 2023)
Total article views: 1,003 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
773
212
18
1,003
61
8
13
HTML: 773
PDF: 212
XML: 18
Total: 1,003
Supplement: 61
BibTeX: 8
EndNote: 13
Views and downloads (calculated since 05 Jan 2023)
Cumulative views and downloads
(calculated since 05 Jan 2023)
Viewed (geographical distribution)
Total article views: 4,403 (including HTML, PDF, and XML)
Thereof 4,338 with geography defined
and 65 with unknown origin.
Total article views: 3,400 (including HTML, PDF, and XML)
Thereof 3,331 with geography defined
and 69 with unknown origin.
Total article views: 1,003 (including HTML, PDF, and XML)
Thereof 1,003 with geography defined
and 0 with unknown origin.
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Effectively assembling multiple models for approaching a benchmark solution remains a...