Citation

Liu LJ, Wu H, Wang J, Yang T. Math. Biosci. Eng. 2020; 17(6): 7302-7331.

Copyright

(Copyright © 2020, American Institute of Mathematical Sciences)

DOI

10.3934/mbe.2020374

PMID

unavailable

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.

Language: en

Keywords

projection pursuit model; quantum particle swarm optimization; resilience capability; subway station project; waterlogging disasters