Format:
1 Online-Ressource (xix, 124 Seiten, 17891 KB)
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Illustrationen, Diagramme
Content:
Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...]
Note:
Kumulative Dissertation
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Volltext: PDF
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Literaturverzeichnis: Seite 97-124
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Dissertation Universität Potsdam 2022
Additional Edition:
Erscheint auch als Druck-Ausgabe Brill, Fabio Alexander Applications of machine learning and open geospatial data in flood risk modelling Potsdam, 2022
Language:
English
Keywords:
Hochwasser
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Hochwasserschaden
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Hochwasservorhersage
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Hochwasserwelle
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Prognose
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Katastrophenrisiko
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Überflutung
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Überschwemmung
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Risikoanalyse
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Überschwemmungsgefahr
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Modellierung
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Maschinelles Lernen
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Angewandte Hydrologie
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Einzugsgebiet
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Überschwemmungsgebiet
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Fernerkundung
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Kartierung
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Geoinformatik
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Geostatistik
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Big Data
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Data Mining
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Datenanalyse
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Deep learning
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Mustererkennung
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Wald
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Stadtregion
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Datenauswertung
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Open Data
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Raumdaten
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Überschwemmungsgefahr
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Hochschulschrift
DOI:
10.25932/publishup-55594
URN:
urn:nbn:de:kobv:517-opus4-555943
URL:
https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-555943
URL:
https://d-nb.info/1264210000/34
Author information:
Kreibich, Heidi 1969-
Author information:
Merz, Bruno
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