@article{ref1,
title="Research on the evaluation of the resilience of subway station projects to waterlogging disasters based on the projection pursuit model",
journal="Mathematical biosciences and engineering",
year="2020",
author="Liu, Lan Jun and Wu, Han and Wang, Junwu and Yang, Tingyou",
volume="17",
number="6",
pages="7302-7331",
abstract="To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a  projection pursuit model (PPM) improved by quantum particle swarm optimization  (QPSO) for the evaluation of the resilience of subway station projects to  waterlogging disasters. In view of the lack of research results related to the  evaluation of the resilience of subway station projects to waterlogging disasters,  16 secondary indicators that affected the ability of subway station projects to  recover from waterlogging disasters were identified from defense, recovery, and  adaptability, for the first time. A PPM improved by QPSO was then proposed to  effectively deal with the high-dimensional data about the resilience of subway  station projects to waterlogging disasters. The QPSO was used to solve the best  projection vector of the PPM, and interpolation algorithm was used to construct the  mathematical model of evaluation. Finally, four station projects of Chengdu Metro  Line 11 in China were selected for a case study analysis. The case study revealed  that, among the secondary indicators, the emergency plan of construction order, the  exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The  values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road  Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2,  1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the  actual disaster situation in the flood season of 2019. In addition, the case study  results showed that QPSO had the advantages of fewer parameter settings and a faster  convergence speed as compared with PSO and the genetic algorithm.<p /> <p>Language: en</p>",
language="en",
issn="1547-1063",
doi="10.3934/mbe.2020374",
url="http://dx.doi.org/10.3934/mbe.2020374"
}