h1

h2

h3

h4

h5
h6
http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png

How to enrich training data for machine learning-based landslide hazard prediction with spatio-temporal precipitation information?

; ; ;

In
Georisk : assessment and management of risk for engineered systems and geohazards Seiten/Artikel-Nr.:[1]-28

ImpressumAbingdon : Taylor and Francis

ISSN1749-9526

Received 05 Jun 2025, Accepted 07 Jan 2026, Published online: 28 Jan 2026

Online
DOI: 10.18154/RWTH-2026-01691
DOI: 10.1080/17499518.2026.2616779

URL: https://publications.rwth-aachen.de/record/1028531/files/1028531.pdf

Einrichtungen

  1. Lehrstuhl für Methoden der Modellbasierten Entwicklung in den Computergestützten Ingenieurwissenschaften (422410)


Thematische Einordnung (Klassifikation)
DDC: 550

OpenAccess:
Download fulltext PDF

Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105029306146
WOS Core Collection: WOS:001673423600001

Interne Identnummern
RWTH-2026-01691
Datensatz-ID: 1028531

Beteiligte Länder
Germany

Lizenzstatus der Zeitschrift

 GO

Creative Commons Attribution CC BY 4.0 ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
QR Code for this record

The record appears in these collections:
Document types > Articles > Journal Articles
Faculty of Mechanical Engineering (Fac.4)
Publication server / Open Access
Public records
Publications database
422410
 Record created 2026-02-11, last modified 2026-03-04
Similar records


OpenAccess:
Download fulltext PDF
Rate this document:

Rate this document:
1
2
3
4
5
 
(Not yet reviewed)