Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Joerg Wicker"'
This paper presents a new framework for the seismic loss prediction of residential buildings in Ōtautahi / Christchurch, New Zealand. It employs data science techniques, geospatial tools, and machine learning (ML) trained on insurance claims data fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bcff441fd6d55b1974d263962bb99bb
https://nhess.copernicus.org/articles/23/1207/2023/
https://nhess.copernicus.org/articles/23/1207/2023/
This paper presents a new framework for the seismic loss prediction of residential buildings in Christchurch, New Zealand. It employs data science techniques, geospatial tools, and machine learning (ML) trained on insurance claims data from the Earth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::760bc2a75985d3afed4f3504b9a2743c
https://nhess.copernicus.org/preprints/nhess-2022-227/
https://nhess.copernicus.org/preprints/nhess-2022-227/
Autor:
Quincy Ma, Joerg Wicker, Liam Wotherspoon, Alonso Gómez-Bernal, Samuel Roeslin, Hugon Juárez-Garcia
Publikováno v:
Earthquake Spectra. 36:314-339
The 2017 Puebla, Mexico, earthquake event led to significant damage in many buildings in Mexico City. In the months following the earthquake, civil engineering students conducted detailed building assessments throughout the city. They collected build
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438869
PKDD/ECML Workshops (2)
PKDD/ECML Workshops (2)
In 2010–2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a717e4fbb2d7c25cdb570977f6dc0d4
https://doi.org/10.1007/978-3-030-43887-6_8
https://doi.org/10.1007/978-3-030-43887-6_8