Issue |
E3S Web Conf.
Volume 314, 2021
The 6th edition of the International Conference on GIS and Applied Computing for Water Resources (WMAD21)
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Article Number | 03005 | |
Number of page(s) | 6 | |
Section | Climate Change & Natural Hazard Related to Water | |
DOI | https://doi.org/10.1051/e3sconf/202131403005 | |
Published online | 26 October 2021 |
KPCA over PCA to assess urban resilience to floods
1
Geosciences, Water and Environment Laboratory Mohammed V University in Rabat, Morocco.
2
National Institute of Statistics and Applied Economics, Rabat, Morocco.
3
Murray Foundation, Brabners LLP, Horton House, Exchange Street, Liverpool L2 3YL, UK
4
CIMA, FCT-Gambelas Campus, University of Algarve, 8005-139 Faro, Portugal
* Corresponding author: narjiss.satour@gmail.com
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.
Key words: Floods / Resilience / KPCA / PCA / Morocco
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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