Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

Kim, Ki Dong; Lee, Saro; Oh, Hanseul; Choi, Jin Kyeong; Won, Joong-Sun

doi:10.1007/s00254-006-0290-5

Cited by 114 publications

(51 citation statements)

References 2 publications

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“…In the present situation, the dependent variable is binary, representing the presence or absence of ground subsidence. The quantitatively relationship between the occurrence and its dependency on several variables can be expressed as [6,15]:…”

Section: Ground Subsidence Hazard Mapping Using Various Modelsmentioning

confidence: 99%

“…Recent studies have analyzed ground subsidence hazards using the results of geological and geotechnical investigations and of probability, statistical, fuzzy algebra, and artificial neural network models in tandem with GIS applications [2,[4][5][6][7][8][9][10][11][12][13][14][15]. Some studies have assessed and identified areas with a high subsidence risk.…”

Section: Introductionmentioning

confidence: 99%

“…As a probabilistic model, Zahiri et al [16] applied the weightsof-evidence technique to derive the rock fall potential associated with mining-induced subsidence. Kim et al [6] and Oh and Lee [14] assessed the spatial ground subsidence hazard potential using GIS techniques with frequency ratio and weights-of-evidence models. Oh et al [15] applied probabilistic-based sensitivity analysis to determine the effect of input factors on ground subsidence hazard maps.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble of ground subsidence hazard maps using fuzzy logic

Park

Lee

Lee³

2014

Open Geosciences

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Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping groundsubsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

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Section: Ground Subsidence Hazard Mapping Using Various Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble of ground subsidence hazard maps using fuzzy logic

Park

Lee

Lee³

2014

Open Geosciences

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“…Conditioning factors can also be combined to generate a map with uniform units, which is then overlaid with the inventory map to provide the density per class. The BSA approach has been used in landslide mapping (Constantin et al, 2011), earthquake studies (Xu et al, 2012b), flood susceptibility mapping (Tehrany et al, 2013), land subsidence (Kim et al, 2006;, and risk analysis (Hu et al, 2009). Numerous studies have been conducted to exploit the potential application of BSA in the hazard domain.…”

Section: Introductionmentioning

confidence: 99%

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

et al. 2015

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Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

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“…Therefore, subsidence effects are very dangerous. Many researchers are focused on hazard and monitoring of subsidence using different techniques (Aksoy et al, 2004;Kim et al, 2006Kim et al, , 2009Zahiri et al, 2006;Luo & Cheng, 2009;Lee et al, 2010;Lee & Oh, 2011;Demirel et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of coal mining subsidence damage in soma-turkey coalfield using fem modelling / ocena szkód górniczych związanych z osiadaniem gruntu w zagłębiu węglowym soma w turcji, z wykorzystaniem metody elementów skończonych

Deliormanli¹

2012

Archives of Mining Sciences

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Soma has mine subsidence problems associated with production. Therefore, property and infrastructure damage will be related with coal-mine subsidence in Soma. In this study, depending on the underground coal mine production method, damage of subsidence on energy line was determined in Soma-Turkey coalfield. Subsidence may still occur in these areas. Therefore, determining subsidence is very important. Finite element methods (FEM) and field measurements were used for determining the parameters of mechanisms. The software, Phase 2 , used for the numerical modeling was developed by Rockscience Inc. The results obtained from modeling have been found to be compatible with previous studies and have been compared with actual field observation.

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Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

Cited by 114 publications

References 2 publications

Ensemble of ground subsidence hazard maps using fuzzy logic

Ensemble of ground subsidence hazard maps using fuzzy logic

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

Assessment of coal mining subsidence damage in soma-turkey coalfield using fem modelling / ocena szkód górniczych związanych z osiadaniem gruntu w zagłębiu węglowym soma w turcji, z wykorzystaniem metody elementów skończonych

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