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Modelling armed conflict risk under climate change with machine learning and time-series data

Authors :
Quansheng Ge
Mengmeng Hao
Fangyu Ding
Dong Jiang
Jürgen Scheffran
David Helman
Tobias Ide
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

Using machine learning, the authors reveal that stable background conditions explain most variation in armed conflict risk worldwide. Positive temperature deviations and precipitation extremes also increase the risk of conflict onset and incidence.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.5e19576afdfc44de9ca627a6606fdbe8
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-30356-x