Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea

Abstract

Quantitative determination of locations vulnerable to ground subsidence at mining regions is necessary for effective prevention. In this paper, a method of constructing subsidence susceptibility maps based on fuzzy relations is proposed and tested at an abandoned underground coal mine in Korea. An advantage of fuzzy combination operators over other methods is that the operation is mathematically and logically easy to understand and its implementation to GIS software is simple and straightforward. A certainty factor analysis was used for estimating the relative weight of eight major factors influencing ground subsidence. The relative weight of each factor was then converted into a fuzzy membership value and integrated as a subsidence hazard index using fuzzy combination operators, which produced coal mine subsidence susceptibility maps. The susceptibility maps were compared with the reported ground subsidence areas, and the results showed high accuracy between our prediction and the actual subsidence. Based on the root mean square error and accuracy in terms of success rates, fuzzy c-operator with a low c value and fuzzy algebraic product operator, specifically, are useful for ground subsidence prediction. Comparing the results of a fuzzy c-operator and a conventional logistic regression model, the performance of the fuzzy approach is comparative to that of a logistic regression model with improved computational. A field survey done in the area supported the method's reliability. A combination of certainty factor analysis and fuzzy relations with a GIS is an effective method to determine locations vulnerable to coal mine subsidence.

Ensemble of ground subsidence hazard maps using fuzzy logic

“…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.…”

“…Esaki et al [7] used a stochastic model to predict subsidence in coal mining areas, while Mancini et al [13] applied a multi-criteria decision model to analyze salt mining activities. Choi et al [11] constructed subsidence susceptibility maps based on fuzzy relations for an AUCM area. Lee and Park [4] applied frequency ratio and decision tree model to mapping GSH maps.…”

“…It can address many of the problems relating to vagueness in spatial information [35] and express the probability of an event occurring as a normalized value between 0 and 1; it is therefore effective for comparing the results with other sites [11]. This paper applies an ensemble method using fuzzy logic to better predict subsidence using a single model.…”

Analysis of surface changes in the Wałbrzych hard coal mining grounds (SW Poland) between 1886 and 2009

“…Djamaluddin et al (2011) used GIS to fa cil i tate sub si dence predic tion us ing the em pir i cal Knothe method and ap pli ca tion of fuzzy logic to iden tify dam age clas si fi ca tion zones. The sub sidence sus cep ti bil ity maps based on fuzzy re la tions were also stud ied by Choi et al (2010). Aus tra lian ex pe ri ences con cern, for ex am ple, ap pli ca bil ity of the sat el lite-based Syn thetic Ap erture Ra dar In ter fer om e try (InSAR) as a method for mon i tor ing of sur face sub si dence caused by min ing of un der ground ore de pos its with the aid of GIS (Jarosz, 2005), and the GIS-based Weights of Ev i dence tech nique to de rive a model of rockfall poten tial as so ci ated with min ing-in duced sub si dence (Zahiri et al, 2008).…”

Deformation information system for facilitating studies of mining-ground deformations, development, and applications

“…For example, the geographic information technologies (GIT) are now widely used to collect and manage spatial data related to mining deformations (Doležalová et al, 2010), as well as geodynamic activity (Bogusz et al, 2013), geographic information systems (GIS) are used to prepare data for analyses in external applications such as simulations with the finite element method (FEM) (Blachowski and Ellefmo, 2012) or artificial neural networks (Kim et al, 2009;Lee et al, 2012), as well as to visualise results of numerical simulations e.g. mining-ground classifications with fuzzy logic (Choi et al, 2010;Malinowska, 2011). GIS are also used for spatial data mining (Blachowski and Stefaniak, 2012) or to perform various ground deformation analyses with spatial data modelling functions, for example determination of subsidence surfaces with interpolation techniques (Kowalczyk et al, 2010) or determination of miningground deformation parameters such as tilt, curvature or horizontal strain (Chrzanowski et al, 2012).…”