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On the Generalization of Agricultural Drought Classification from Climate Data. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2021 "Tackling Climate Change with Machine Learning"

Gottfriedsen, Julia Sophia and Berrendorf, Max and Gentine, Pierre and Hassler, Birgit and Reichstein, Markus and Weigel, Katja and Eyring, Veronika (2021) On the Generalization of Agricultural Drought Classification from Climate Data. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2021 "Tackling Climate Change with Machine Learning". Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 6. - 14. Dez 2021, remote.

Full text not available from this repository.

Official URL: https://neurips.cc/

Abstract

Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon SMI from a hydrological model, which is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with landuse information from MODIS satellite observation, we compare different models with and without sequential inductive bias in their ability to classify droughts based on SMI. We use PR-AUC and Macro F1 Score as evaluation measures to account for the class imbalance and obtain promising results despite a challenging time-based split. We show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models.

Item URL in elib:https://elib.dlr.de/145433/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:On the Generalization of Agricultural Drought Classification from Climate Data. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2021 "Tackling Climate Change with Machine Learning"
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gottfriedsen, Julia SophiaDLR, IPAUNSPECIFIED
Berrendorf, Maxberrendorf (at) dbs.ifi.lmu.deUNSPECIFIED
Gentine, PierreDepartment of Earth and Environmental Engineering, Columbia University, New York, USAhttps://orcid.org/0000-0002-0845-8345
Hassler, BirgitDLR, IPAUNSPECIFIED
Reichstein, MarkusMax-Planck-Institute for Biogeochemistry, Jena, Germanyhttps://orcid.org/0000-0001-5736-1112
Weigel, KatjaDLR, IPA und Univ. BremenUNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885
Date:December 2021
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Machine Learning, KI, Extreme Events, ERA5 Land, Climate Change
Event Title:Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS)
Event Location:remote
Event Type:international Conference
Event Dates:6. - 14. Dez 2021
Organizer:Marc'Aurelio Ranzato, DeepMind
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Gottfriedsen, Julia Sophia
Deposited On:10 Nov 2021 09:46
Last Modified:24 Nov 2021 13:25

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