Literature DB >> 29898510

Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach.

Hai-Min Lyu1, Wen-Juan Sun2, Shui-Long Shen3, Arul Arulrajah4.   

Abstract

Metro system is a vital component of mass transportation infrastructure, providing crucial social and economic service in urban area. Flood events may cause functional disruptions to metro systems; therefore, a better understanding of their vulnerability would enhance their resilience. A comparative study of flood risk in metro systems is presented using the analytic hierarchy process (AHP) and the interval AHP (I-AHP) methods. The flood risk in the Guangzhou metro system is evaluated according to recorded data. Evaluated results are validated using the flood event occurred in Guangzhou on May 10, 2016 (hereinafter called "May 10th event"), which inundated several metro stations. The flood risk is assessed within a range of 500 m around the metro line. The results show that >50% of metro lines are highly exposed to flood risk, indicating that the Guangzhou metro system is vulnerable to flood events. Comparisons between results from AHP and I-AHP show that the latter yields a wider range of high flooding risk than the former.
Copyright © 2018 Elsevier B.V. All rights reserved.

Keywords:  Flood risk assessment; GIS; I-AHP; Metro system

Year:  2018        PMID: 29898510     DOI: 10.1016/j.scitotenv.2018.01.138

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  10 in total

1.  Flood Risk Management in Canada's Prairie Provinces: an Analysis of Decision-Maker Priorities and Policy Preferences.

Authors:  Alasdair Morrison; Bram F Noble; Cherie J Westbrook
Journal:  Environ Manage       Date:  2019-09-30       Impact factor: 3.266

2.  Flood Risk Evaluation in Urban Spaces: The Study Case of Tormes River (Salamanca, Spain).

Authors:  Marco Criado; Antonio Martínez-Graña; Javier Sánchez San Román; Fernando Santos-Francés
Journal:  Int J Environ Res Public Health       Date:  2018-12-20       Impact factor: 3.390

3.  Data on point cloud scanning and ground radar of composite lining in jointly constructed tunnel.

Authors:  Jia-Xuan Zhang; Ning Zhang; Ye-Shuang Xu
Journal:  Data Brief       Date:  2022-02-25

Review 4.  A systematic review of the flood vulnerability using geographic information system.

Authors:  Shiau Wei Chan; Sheikh Kamran Abid; Noralfishah Sulaiman; Umber Nazir; Kamran Azam
Journal:  Heliyon       Date:  2022-03-08

5.  Evolution and analysis of urban resilience and its influencing factors: a case study of Jiangsu Province, China.

Authors:  Xiaotong You; Yanan Sun; Jiawei Liu
Journal:  Nat Hazards (Dordr)       Date:  2022-05-04

6.  Cascading Failures and Vulnerability Evolution in Bus⁻Metro Complex Bilayer Networks under Rainstorm Weather Conditions.

Authors:  Fei Ma; Fei Liu; Kum Fai Yuen; Polin Lai; Qipeng Sun; Xiaodan Li
Journal:  Int J Environ Res Public Health       Date:  2019-01-24       Impact factor: 3.390

7.  Land Subsidence Control Zone and Policy for the Environmental Protection of Shanghai.

Authors:  Xi-Cun He; Tian-Liang Yang; Shui-Long Shen; Ye-Shuang Xu; Arul Arulrajah
Journal:  Int J Environ Res Public Health       Date:  2019-07-31       Impact factor: 3.390

8.  Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP.

Authors:  Hai-Min Lyu; Shui-Long Shen; Annan Zhou; Jun Yang
Journal:  Data Brief       Date:  2019-12-17

9.  Data in flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach.

Authors:  Hai-Min Lyu; Shui-Long Shen; Annan Zhou; Wan-Huan Zhou
Journal:  Data Brief       Date:  2019-09-03

10.  A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China.

Authors:  Junfei Chen; Qian Li; Huimin Wang; Menghua Deng
Journal:  Int J Environ Res Public Health       Date:  2019-12-19       Impact factor: 3.390

  10 in total

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