Hazard assessment model of ground subsidence coupling AHP, RS and GIS – A case study of Shanghai

[Display omitted] •GSREI regarding accumulated subsidence, subsidence rate, and elevation.•VEI from population, GDP, construction land, viaduct and metro density.•Risk assessment model and the GSHI index. Urban ground subsidence (GS) causes ground elevation loss, threatens the safe operation of vari...

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Published inGondwana research Vol. 117; pp. 344 - 362
Main Authors Zhang, Zhihua, Zhang, Shaobin, Hu, Changtao, Zhang, Xinxiu, Yang, Shuwen, Yan, Haowen, Zhang, Zhen
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2023
Subjects
Analytic hierarchy process (AHP)
Geographic information system (GIS)
Ground subsidence
Ground subsidence hazard index (GSHI)
Remote sensing (RS)
Ground subsidence
Ground subsidence hazard index (GSHI)
Geographic information system (GIS)
Analytic hierarchy process (AHP)
Remote sensing (RS)
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Abstract [Display omitted] •GSREI regarding accumulated subsidence, subsidence rate, and elevation.•VEI from population, GDP, construction land, viaduct and metro density.•Risk assessment model and the GSHI index. Urban ground subsidence (GS) causes ground elevation loss, threatens the safe operation of various facilities, and affects surface runoff and hydrological circulation. Therefore, it is vital to monitor the current status of GS to predict and evaluate potential risks to sustainable urban development. Sentinel SAR images from 2019 to 2020 combined with the data from the 2019–2020 Statistical Yearbook were used as data sources. Based on PS-InSAR and SBAS-InSAR, GIS was integrated with the AHP method. A ground subsidence risk evaluation index (REI) suitable for Shanghai was developed regarding three aspects of the amount of accumulated land subsidence (RS obtained): subsidence rate, and terrain elevation. The vulnerability evaluation index (VEI) was established from five aspects: population density, GDP per unit area, the proportion of construction land, viaduct density, and metro density. We introduced the fuzzy AHP method to complete the risk assessment of ground subsidence in Shanghai and obtained the ground subsidence hazard index (GSHI). We used GIS superimposed remote sensing images to visualize the individual evaluation indicators REI, VEI, and GSHI. Using GIS superimposed remote sensing images, various evaluation indicators and GSHI were visualized. Based on the evaluation results, targeted prevention and control recommendations are provided. The results of this research can provide a basis for scientific decision-making regarding Shanghai’s territorial spatial planning and socio-economic development.
AbstractList [Display omitted] •GSREI regarding accumulated subsidence, subsidence rate, and elevation.•VEI from population, GDP, construction land, viaduct and metro density.•Risk assessment model and the GSHI index. Urban ground subsidence (GS) causes ground elevation loss, threatens the safe operation of various facilities, and affects surface runoff and hydrological circulation. Therefore, it is vital to monitor the current status of GS to predict and evaluate potential risks to sustainable urban development. Sentinel SAR images from 2019 to 2020 combined with the data from the 2019–2020 Statistical Yearbook were used as data sources. Based on PS-InSAR and SBAS-InSAR, GIS was integrated with the AHP method. A ground subsidence risk evaluation index (REI) suitable for Shanghai was developed regarding three aspects of the amount of accumulated land subsidence (RS obtained): subsidence rate, and terrain elevation. The vulnerability evaluation index (VEI) was established from five aspects: population density, GDP per unit area, the proportion of construction land, viaduct density, and metro density. We introduced the fuzzy AHP method to complete the risk assessment of ground subsidence in Shanghai and obtained the ground subsidence hazard index (GSHI). We used GIS superimposed remote sensing images to visualize the individual evaluation indicators REI, VEI, and GSHI. Using GIS superimposed remote sensing images, various evaluation indicators and GSHI were visualized. Based on the evaluation results, targeted prevention and control recommendations are provided. The results of this research can provide a basis for scientific decision-making regarding Shanghai’s territorial spatial planning and socio-economic development.
Author Zhang, Shaobin
Yan, Haowen
Zhang, Zhen
Yang, Shuwen
Hu, Changtao
Zhang, Xinxiu
Zhang, Zhihua
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Keywords Ground subsidence
Ground subsidence hazard index (GSHI)
Geographic information system (GIS)
Analytic hierarchy process (AHP)
Remote sensing (RS)
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Snippet [Display omitted] •GSREI regarding accumulated subsidence, subsidence rate, and elevation.•VEI from population, GDP, construction land, viaduct and metro...
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SourceType Enrichment Source
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StartPage 344
SubjectTerms Analytic hierarchy process (AHP)
Geographic information system (GIS)
Ground subsidence
Ground subsidence hazard index (GSHI)
Remote sensing (RS)
Title Hazard assessment model of ground subsidence coupling AHP, RS and GIS – A case study of Shanghai
URI https://dx.doi.org/10.1016/j.gr.2023.01.014
Volume 117
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