Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea

Fadhillah, Muhammad Fulki; Achmad, Arief Rizqiyanto; Lee, Chang-Wook

doi:10.3390/rs12213505

Cited by 41 publications

(14 citation statements)

References 89 publications

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“…For example, the very high and high risk levels of the Batong Line located in Chaoyang increased by 3.4 and 3.79 km 2 under the land subsidence scenario, with growth rates of 2615.38% and 119.56%, respectively. Thus, land subsidence is a crucial indicator of aggravating the flood risk level of the subway system [35,55].…”

Section: Land Subsidence Aggravates the Flood Risk Level Of The Subwamentioning

confidence: 99%

“…Furthermore, it is unclear to what extent the land subsidence factor exacerbates the flood risk of subway systems. At least 200 cities worldwide have built subways, including New York [21], London [32], Beijing [33], Shanghai [34], and Seoul metropolitan area [35], all of which have suffered severe land subsidence. Land subsidence is an environmental geological phenomenon induced by over-exploiting underground resources and building pressure from above [36].…”

Section: Introductionmentioning

confidence: 99%

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Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Wang

Liu

et al. 2021

Remote Sensing

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Flooding is one of the most destructive natural events that severely damage the ground and inundate underground infrastructure. Subway systems in metropolitan areas are susceptible to flooding, which may be exacerbated when land subsidence occurs. However, previous studies have focused on flood risk evaluation on regional/watershed-scales and land subsidence monitoring in plains, instead of on subway flood risk evaluation and how land subsidence aggravates the flood risk in subway systems. Using the proposed risk indicators and field survey data, we present a method assessing the flood risk of metropolitan subway systems under a subsidence condition based on the fuzzy analytic hierarchy process (FAHP) combined with a geographic information system (GIS). We use the regional risk level within the 500 m buffer zone of the subway line to depict the flood risk of the subway system. The proposed method was used to evaluate the flood risk of the Beijing subway system. The results show that the flood risks of the Beijing subway show a ring-like distribution pattern—risk levels decreasing from the central urban area to the suburbs. Very high and high risks are mainly located within third and fourth ring roads, accounting for 63.58% (29.40 km2) and 63.83% (81.19 km2) of the total area. Land subsidence exacerbated the Beijing subway system’s flood risk level—the moderate to very high risk increased by 46.88 km2 (16.33%), indicating that land subsidence is an essential factor affecting the flood risk level of subway systems. In addition to enhancing flood warnings, future subway flooding could be reduced by elevating the height of the stations’ exit (entrance) and installing water stop plates and watertight doors. This study is of great significance for flood warning and prevention in the Beijing subway system; it provides a theoretical basis for flood risk evaluation in other metropolitan areas.

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Section: Land Subsidence Aggravates the Flood Risk Level Of The Subwamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Wang

Liu

et al. 2021

Remote Sensing

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“…Furthermore, several studies confirm the potential of applying Machine Learning (ML) algorithms and PSI techniques to assess resilience of infrastructures. [33][34][35].…”

Section: Multi-temporal Insar For Transport Infrastructure Monitoringmentioning

confidence: 99%

Monitoring of bridges by MT-InSAR and unsupervised machine learning clustering techniques

Gagliardi¹,

Tosti

Ciampoli

et al. 2021

Earth Resources and Environmental Remote Sensing/Gis Applications XII

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Continuous monitoring of critical infrastructures is crucial to prevent catastrophic events such as collapse of viaducts and prioritising maintenance interventions. However, developing effective monitoring approaches must rely on the collection of numerous information, such as the time series of structural deformations. In this context, various groundbased non-destructive testing (NDT) methods have been used in monitoring the structural integrity of transport infrastructures. These require routine and systematic application at the network level over long periods of time to build up a solid database of information, involving many efforts from stakeholders and asset owners in the sector. To this effect, satellite-based remote sensing techniques, such as the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR), have gained momentum due to the provision of accurate cumulative structural displacements in bridges. Although the application of the InSAR monitoring technique is established, this is limited by the high amount of time required for the interpretation of data with high spatial and temporal density. This research aims to demonstrate the viability of MT-InSAR techniques for the structural assessment of bridges and the monitoring of damage by structural subsidence, using high-resolution SAR datasets, integrated with complementary Ground-Based (GB) information. To this purpose, high-resolution SAR dataset of the COSMO-SkyMed (CSK) mission provided by the Italian Space Agency (ASI), were acquired and processed in the framework of the ASI-Open Call approved Project "MoTiB" (ID 742). To elaborate, a Persistent Scatterer Interferometry (PSI) analysis is applied to identify and monitor the structural displacements at the Rochester Bridge, in Rochester, Kent, UK. To explore the viability of Machine Learning algorithms in detecting critical scenarios in the monitoring phases, an Unsupervised ML Clustering approach, which generates homogeneous and well-separated clusters, is implemented. Each PS data-point is allocated to specific cluster groups, based on individual deformation trend features and the values of displacements from the historical time-series. This research paves the way for the development of a novel interpretation approach relying on the integration between remote-sensing technologies and on-site surveys to improve upon current maintenance strategies for bridges and transport assets.

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“…Therefore, InSAR has also been applied to the monitoring of land subsidence during subway operations. Fadhillah et al [14] used the InSAR technique to generate deformation time series maps using the Stamfs method to investigate subsidence in four areas. Shi et al [15] used the SBAS-InSAR technique to analyze subsidence in Xi'an and to predict land subsidence.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Study of Long-Term Land Subsidence during Subway Operation in High-Density Urban Areas Based on DInSAR-GPS-GIS Technology and Numerical Simulation

Yu¹,

Chen²,

Zou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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During subway operation, various factors will cause long-term land subsidence, such as the vibration subsidence of foundation soil caused by train vibration load, incomplete consolidation deformation of foundation soil during tunnel construction, dense buildings and structures in the vicinity of the tunnel, and changes in water level in the stratum where the tunnel is located. The monitoring of long-term land subsidence during subway operation in high-density urban areas differs from that in low-density urban construction areas. The former is the gathering point of the entire urban population. There are many complex buildings around the project, busy road traffic, high pedestrian flow, and less vegetation cover. Several existing items require monitoring. However, monitoring distance is long, and providing early warning is difficult. This study uses the 2.8 km operation line between Wulin Square station and Ding'an Road station of Hangzhou Subway Line 1 as an example to propose the integrated method of DInSAR-GPS-GIS technology and the key algorithm for long-term land subsidence deformation. Then, it selects multiscene image data to analyze long-term land subsidence of high-density urban areas during subway operation. Results show that long-term land subsidence caused by the operation of Wulin Square station to Ding'an Road station of Hangzhou Subway Line 1 is small, with maximum subsidence of 30.64 mm, and minimum subsidence of 11.45 mm, and average subsidence ranging from 19.27 to 21.33 mm. And FLAC3D software was used to verify the monitoring situation, using the geological conditions of the soil in the study area and the tunnel profile to simulate the settlement under vehicle load, and the simulation results tended to be consistent with the monitoring situation.

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Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea

Cited by 41 publications

References 89 publications

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Monitoring of bridges by MT-InSAR and unsupervised machine learning clustering techniques

Monitoring Study of Long-Term Land Subsidence during Subway Operation in High-Density Urban Areas Based on DInSAR-GPS-GIS Technology and Numerical Simulation

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