KPCA over PCA to assess urban resilience to floods

Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensiona...

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Autores principales:	Satour Narjiss, Benyacoub Badreddine, El Mahrad Badr, Kacimi Ilias
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	floods resilience kpca pca morocco Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/25a142adf8e442dfa6445ccf9790f0c2
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id	oai:doaj.org-article:25a142adf8e442dfa6445ccf9790f0c2
record_format	dspace
spelling	oai:doaj.org-article:25a142adf8e442dfa6445ccf9790f0c22021-11-08T15:19:09ZKPCA over PCA to assess urban resilience to floods2267-124210.1051/e3sconf/202131403005https://doaj.org/article/25a142adf8e442dfa6445ccf9790f0c22021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/90/e3sconf_wmad2021_03005.pdfhttps://doaj.org/toc/2267-1242Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensionality reduction, the PCA reduces datasets to representations consisting of principal components. Kernel PCA (KPCA) is the nonlinear form of PCA, which efficiently presents a complicated data in a lower dimensional space. In this work the KPCA techniques was applied to measure and map flood resilience across a local level. Therefore, it aims to improve the performance achieved by non-linear PCA application, compared to standard PCA. Twenty-one resilience indicators were gathered, including social, economic, physical, and natural components into a composite index (Flood resilience Index). The experimental results demonstrate the KPCA performance to get a better Flood Resilience Index, guiding q decision making to strengthen the flood resilience in our case of study of M’diq-Fnideq and martil municipalities in Northern of Morocco.Satour NarjissBenyacoub BadreddineEl Mahrad BadrKacimi IliasEDP SciencesarticlefloodsresiliencekpcapcamoroccoEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 314, p 03005 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	floods resilience kpca pca morocco Environmental sciences GE1-350
spellingShingle	floods resilience kpca pca morocco Environmental sciences GE1-350 Satour Narjiss Benyacoub Badreddine El Mahrad Badr Kacimi Ilias KPCA over PCA to assess urban resilience to floods
description	Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensionality reduction, the PCA reduces datasets to representations consisting of principal components. Kernel PCA (KPCA) is the nonlinear form of PCA, which efficiently presents a complicated data in a lower dimensional space. In this work the KPCA techniques was applied to measure and map flood resilience across a local level. Therefore, it aims to improve the performance achieved by non-linear PCA application, compared to standard PCA. Twenty-one resilience indicators were gathered, including social, economic, physical, and natural components into a composite index (Flood resilience Index). The experimental results demonstrate the KPCA performance to get a better Flood Resilience Index, guiding q decision making to strengthen the flood resilience in our case of study of M’diq-Fnideq and martil municipalities in Northern of Morocco.
format	article
author	Satour Narjiss Benyacoub Badreddine El Mahrad Badr Kacimi Ilias
author_facet	Satour Narjiss Benyacoub Badreddine El Mahrad Badr Kacimi Ilias
author_sort	Satour Narjiss
title	KPCA over PCA to assess urban resilience to floods
title_short	KPCA over PCA to assess urban resilience to floods
title_full	KPCA over PCA to assess urban resilience to floods
title_fullStr	KPCA over PCA to assess urban resilience to floods
title_full_unstemmed	KPCA over PCA to assess urban resilience to floods
title_sort	kpca over pca to assess urban resilience to floods
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/25a142adf8e442dfa6445ccf9790f0c2
work_keys_str_mv	AT satournarjiss kpcaoverpcatoassessurbanresiliencetofloods AT benyacoubbadreddine kpcaoverpcatoassessurbanresiliencetofloods AT elmahradbadr kpcaoverpcatoassessurbanresiliencetofloods AT kacimiilias kpcaoverpcatoassessurbanresiliencetofloods
_version_	1718441926449954816

KPCA over PCA to assess urban resilience to floods

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