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Augmenting natural hazard exposure modelling using natural language processing

Schembri, J; Gentile, R; (2024) Augmenting natural hazard exposure modelling using natural language processing. International Journal of Disaster Risk Reduction , 101 , Article 104202. 10.1016/j.ijdrr.2023.104202. Green open access

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Abstract

Natural hazard exposure modelling involves constructing databases that describe the elements (people and built environment) exposed to some hazard in a selected location. These databases are often constructed using information from censuses, cadastral data, or satellite imagery. In this work, we suggest complementing hazard exposure modelling using an alternative and unconventional data source: the text components of building permits. The proposed methodology, Natural Language Processing for the Global Exposure Database (NLP4GED), adopts natural language processing techniques to extract building-by-building exposure attributes in line with the GED4ALL taxonomy (Global Exposure Database for ALL). This three-step methodology involves using: a classifier to filter permits potentially containing exposure information; a clustering algorithm to identify semantically similar permits; and regular expressions (or regex) to extract exposure-attributes. As an illustrative application, we apply NLP4GED to wrangle an unstructured real-world dataset of 100,989 building permits in Malta. We effectively provide relevant exposure attributes (i.e., year of construction, building height, and occupancy) for 23,076 buildings presented in a geographic information system (GIS) environment.

Type: Article
Title: Augmenting natural hazard exposure modelling using natural language processing
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijdrr.2023.104202
Publisher version: http://dx.doi.org/10.1016/j.ijdrr.2023.104202
Language: English
Additional information: © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction
URI: https://discovery.ucl.ac.uk/id/eprint/10186500
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