Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran)

Chitsazan, Manouchehr; Rahmani, Gholamreza; Ghafoury, Hamidreza

doi:10.1007/s10661-021-09645-3

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Various studies have proposed methods to develop susceptibility maps for land subsidence using different techniques, or land subsidence hazard maps [9], which is a problem affecting construction and infrastructure in many parts of the world [10][11][12][13][14][15][16][17][18]. However, few have tackled the problem of generating or reactivating the faults and fractures associated with subsidence.…”

Section: Introductionmentioning

confidence: 99%

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

et al. 2023

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Land subsidence in the Aguascalientes Valley, documented since the 1980s, has developed a large number of discontinuities that damage infrastructure. There is currently no methodology to accurately predict the site and time at which a discontinuity will occur, making it difficult to make decisions in urban planning or risk management. However, it is possible to determine the susceptibility of an area to the generation of fractures based on the factors associated with their formation. This study presents a zoning method based on the ground failure susceptibility index (GFSI) in the city of Aguascalientes, using the frequency ratio (FR) method and employing the depth of the basement, the subsidence rate, the subsidence gradient, and the groundwater level drawdown as variables. The zoning method included three categories of land subsidence susceptibility to fracturing, moderate, high, and very high, which were divided using the first (3.76) and second (4.24) quartiles of the GFSI. The zoning method was created with the discontinuities reported in 2010 and was validated with data from 2022. The results obtained show that 11.19% of the discontinuities developed between 2010 and 2022 were located in a zone of moderate susceptibility, 41.97% were located in a zone of high susceptibility, and 46.87% were located in a zone classified as having very high susceptibility.

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Section: Introductionmentioning

confidence: 99%

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

et al. 2023

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“…There are threemain approaches to land subsidence prediction: physical models [36], mathematical statistical models [37], and machine learning models [38][39][40][41][42]. The physical model predicts land subsidence based on a series of complex geological and hydrological characteristics of physical parameters through field investigations and experiments.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Land Subsidence Monitoring and Prediction Based on SBAS-InSAR Technology with Time-Series Sentinel-1A Satellite Data

et al. 2023

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Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

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Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Zhang

Arabameri

Santosh

et al. 2023

Environ Sci Pollut Res

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Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran)

Cited by 6 publications

References 20 publications

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

Large-Scale Land Subsidence Monitoring and Prediction Based on SBAS-InSAR Technology with Time-Series Sentinel-1A Satellite Data

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

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