KPCA over PCA to assess urban resilience to floods

Abstract

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.

Sarcasm‐based tweet‐level stress detection

“…In linear PCA, the data is projected onto a linear subspace of lower dimensions (Han et al, 2011). But linear PCA does not perform well when the data is linearly non‐separable (Satour et al, 2021).…”

“…Dimensionality reduction analysis for improving the performance of tweet‐level stress detection is not addressed in the literature (KVTKN & Ramakrishnudu, 2022; Lin et al, 2016; Lin et al, 2017; Lin, Jia, Guo, Xue, Huang, et al, 2014; Pratama & Sarno, 2015; Xue et al, 2016; Zhao et al, 2015). It is observed that Kernel Principal Component Analysis (PCA) helps in obtaining the dimensionality reduction when the classes are linearly non‐separable (Satour et al, 2021). The reasons for choosing PCA over other dimensionality reduction methods are listed as follows: There are many dimensionality reduction techniques that rely on the use of classifiers, such as forward selection, chi‐square, backward selection, and so forth.…”