Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Lily‐belle Sweet"'
Autor:
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, Alexander Brenning, Wantong Li, Markus Reichstein, Joachim Denzler, Wei Shangguan, Guo Yu, Feini Huang, Jakob Zscheischler
Publikováno v:
Earth's Future, Vol 12, Iss 7, Pp n/a-n/a (2024)
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but
Externí odkaz:
https://doaj.org/article/eb521b00c73e49509e141f1f17007bc6
Autor:
Mohit Anand, Friedrich J. Bohn, Gustau Camps-Valls, Rico Fischer, Andreas Huth, Lily-belle Sweet, Jakob Zscheischler
Publikováno v:
Environmental Data Science, Vol 3 (2024)
Globally, forests are net carbon sinks that partly mitigates anthropogenic climate change. However, there is evidence of increasing weather-induced tree mortality, which needs to be better understood to improve forest management under future climate
Externí odkaz:
https://doaj.org/article/982e532de0704f5394fbc67fc346660b
Forest health is affected by many interacting and correlated weather variables over multiple temporal scales. Climate change affects weather conditions and their dependencies. To better understand future forest health and status, an improved scientif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53af1643101555a493d0004e68aeeb77
https://doi.org/10.5194/egusphere-egu23-10219
https://doi.org/10.5194/egusphere-egu23-10219
Machine learning models are able to capture highly complex, nonlinear relationships, and have been used in recent years to accurately predict crop yields at regional and national scales. This success suggests that the use of ‘interpretable’ or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31ccb17a350564a0830d2c709d810df0
https://doi.org/10.5194/egusphere-egu23-8479
https://doi.org/10.5194/egusphere-egu23-8479