Title: Comprehensive assessment and prediction of urban resilience: a case study of China

Authors: Yao Wang; Zhe Liu

Addresses: School of Economics and Management, Jilin Jianzhu University, Changchun, Jilin, 130118, China ' School of Economics and Management, Jilin Jianzhu University, Changchun, Jilin, 130118, China

Abstract: Urban resilience is widely used to describe the capability of cities to fend off internal and external risks, reduce losses, and recover quickly. The assessment and prediction of urban resilience can help cities develop strategies and plans to deal with unknown disaster risks. This paper employs the method of combining the entropy weights method (EVM) and technology for order preference by similarity to an ideal solution to establish the urban resilience comprehensive evaluation model and uses the grey prediction model and back propagation neural network to predict the urban disaster resilience value. The results have suggested that the resilience of cities in different dimensions is generally high in eastern China, while the urban resilience in northeast China is not higher than the average, and the regions with an average level of resilience are concentrated in central China.

Keywords: urban resilience; EWM; entropy weights method; TOPSIS; technology for order preference by similarity to an ideal solution; BP neural network; grey model.

DOI: 10.1504/IJCSM.2023.131439

International Journal of Computing Science and Mathematics, 2023 Vol.17 No.3, pp.229 - 240

Received: 06 Nov 2021
Accepted: 20 Mar 2022
Published online: 13 Jun 2023 *

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