Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s35147-022-1008-7
Titel (primär) Automatized drought impact detection using natural language processing
Autor Sodoge, J.; de Brito, M.M.; Kuhlicke, C.
Quelle WasserWirtschaft
Erscheinungsjahr 2022
Department SUSOZ
Band/Volume 112
Heft S1
Seite von 30
Seite bis 31
Sprache englisch
Topic T5 Future Landscapes
Abstract Our Team developed a method for the automatized detection of drought impacts based on newspaper articles. The method can extract different classes of drought impacts and their geographic and temporal scope from text data. We generated a multi-sectoral dataset of drought impacts in Germany from 2000 to 2021.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26210
Sodoge, J., de Brito, M.M., Kuhlicke, C. (2022):
Automatized drought impact detection using natural language processing
WasserWirtschaft 112 (S1), 30 - 31