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	Author	López-Catalán, B.; Bañuls, V.A.
	Title	A Topic Modeling Approach for Extracting Key City Resilience Indicators			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	944-952
	Keywords	Urban Resilience; Machine Learning; Indicators; Topic Modeling; KCR
	Abstract	In the field of urban resilience, there is a great diversity of approaches to measuring the level of resilience in cities. This information is scattered among reports and academic articles. In this ongoing research paper, we explore the potential of Topic Modeling to analyze this information, in order to determine cluster indicators for a set of academic papers and resilience frameworks. These clusters are referred to as Key City Resilience Indicators (KCRI), which are used as reference to facilitate the measurement of urban resilience regardless of the context, including all the key dimensions required for cities to achieve resilience. Topic modeling outcomes can be used to generate indicators based on each topic or to automatically classify a new set of indicators in each of the established topics. These results can be applied to any resilience framework
	Address	Universidad Pablo de Olavide
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	AI for Disaster Risk Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/DTVH1466			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2578
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