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Enhancing Natural-Hazard Exposure Modeling Using Natural Language Processing: a Case-Study for Maltese Planning Applications

Schembri, J; Gentile, R; Galasso, C; (2022) Enhancing Natural-Hazard Exposure Modeling Using Natural Language Processing: a Case-Study for Maltese Planning Applications. In: Procedia Structural Integrity. (pp. pp. 1720-1727). Elsevier Green open access

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Abstract

The algorithmic processing of written language for tools such as predictive text, sentiment analysis, and translation services has become commonplace. The segment of computer science concerned with the interpretation of human language, NLP (Natural Language Processing), is a versatile and fast-developing field. In this paper, NLP is deployed unconventionally to gather insights into a building's multi-hazard exposure characteristics consistent with the GED4ALL attributes. NLP is used in this study to "read" the contents of digitally-submitted planning applications made on the Maltese archipelago. Maltese architects/engineers submit a concise but detailed description of the proposed works on any given site as part of a planning process. It is suggested that valuable insights exist within this description that can assist in classifying buildings within the bounds of the GED4ALL taxonomy. NLP can be used to layer additional, building-by-building information onto existing exposure models based on more conventional data. Although the results of this study are preliminary, NLP may prove a valuable tool for enhancing exposure modeling for multi-hazard risk quantification and management.

Type: Proceedings paper
Title: Enhancing Natural-Hazard Exposure Modeling Using Natural Language Processing: a Case-Study for Maltese Planning Applications
Event: XIX ANIDIS Conference, Seismic Engineering in Italy
ISBN-13: 9781713870418
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.prostr.2023.01.220
Publisher version: https://doi.org/10.1016/j.prostr.2023.01.220
Language: English
Additional information: © 2023 The Author(s). Published by Elsevier B.V. under a Creative Commons license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Natural-hazard modeling, natural language processing, text mining, exposure modeling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
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/10171402
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