Publikationsdatum:
2023-06-16
Beschreibung:
Landslide research relies on landslide inventories for a multitude of spatial, temporal, or process analyses. Generally, it takes high effort to populate a landslide inventory with relevant data. In this context, the present work investigated an effective way to handle vast amounts of automatically acquired digital data for landslide inventories by the use of machine learning algorithms and information filtering. Between July 2017 and February 2019, a keyword alert system provided 4381 documents that were automatically processed to detect landslide events in Germany. Of all those documents, 91% were automatically recognized as irrelevant or duplicates; thereby, the data volume was significantly reduced to contain only actual landslide documents. Moreover, it was shown that inclusion of the document’s images into the automated process chain for information filtering is recommended, since otherwise unobtainable important information was found in them. Compared with manual methods, the automated process chain eliminated personal idiosyncrasies and human error and replaced it with a quantifiable machine error. The applied individual algorithms for natural language processing, information retrieval, and classification have been tried and tested in their respective fields. Furthermore, the proposed method is not restricted to a specific language or region. All languages on which these algorithms are applicable can be used with the proposed method and the training of the process chain can take any geographical restriction into account. Thus, the present work introduced a method with a quantifiable error to automatically classify and filter large amounts of data during automated digital data acquisition for landslide inventories.
Beschreibung:
Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659
Beschreibung:
Niedersächsische Ministerium für Wissenschaft und Kultur
http://dx.doi.org/10.13039/100011937
Schlagwort(e):
ddc:551.3
;
Landslide inventory
;
Data acquisition
;
Machine learning
;
Document classification
;
Information filtering
Sprache:
Englisch
Materialart:
doc-type:article
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