Hyun-Joo Oh

Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping.

The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of ≥6.25 m3/d/m (Case 1) and T values of ≥3.79 m2/d (Case 3) corresponding to a yield (Y) of ≥500 m3/d, and SPC values of ≥3.75 m3/d/m (Case 2) and T values of ≥2.61 m2/d (Case 4) corresponding to a Y of ≥300 m3/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W+ and W- of each factors rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 71.20% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration.

Ensemble-Based Landslide Susceptibility Maps in Jinbu Area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.

Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis

The purpose of this study is to detect landslide locations using web-based digital aerial photographs and to map landslide susceptibility using landslide locations in Jinbu, Korea. The landslide susceptibility map was generated and validated using frequency ratio, weight of evidence, logistic regression and artificial neural network models with a geographic information system (GIS). The landslide locations were identified in the study area from interpretation of digital aerial photographs that were provided on an Internet portal (http://map.daum.net) and checked by field survey. A spatial database of the topography, soil, forest, geology and land use was constructed and landslide-related factors were extracted. Using these factors, landslide susceptibility was analysed using four models. Seventy percent of the landslides were used in landslide susceptibility mapping and the remaining 30% were used for validation. The validation result showed that the frequency ratio, weight of evidence, logistic regression and artificial neural network models had 84.94%, 82.82%, 87.72% and 81.44% accuracies, respectively, representing an overall satisfactory agreement of more than 80%, with the logistic regression model giving the best result. The maps generated could be used to estimate the risk to population, property and existing infrastructure such as the transportation network.

Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat. The control factors were compiled as a spatial database from remotely sensed data combined with GIS analysis. The relative weight of each factor was integrated as a species potential index (SPI), which produced habitat potential maps. The maps were compared with the surveyed habitat locations, revealing a strong correlation between the potential maps and species locations. The combination of a GIS-based weights-of-evidence model and remote sensing techniques is an effective method in determining areas of macrobenthos habitat potential in a tidal flat setting.

Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling

This paper assesses the performance of the landslide susceptibility analysis using frequency ratio (FR) with an iterative random sampling. A pair of before-and-after digital aerial photographs with 50 cm spatial resolution was used to detect landslide occurrences in Yongin area, Korea. Iterative random sampling was run ten times in total and each time it was applied to the training and validation datasets. Thirteen landslide causative factors were derived from the topographic, soil, forest, and geological maps. The FR scores were calculated from the causative factors and training occurrences repeatedly ten times. The ten landslide susceptibility maps were obtained from the integration of causative factors that assigned FR scores. The landslide susceptibility maps were validated by using each validation dataset. The FR method achieved susceptibility accuracies from 89.48% to 93.21%. And the landslide susceptibility accuracy of the FR method is higher than 89%. Moreover, the ten times iterative FR modeling may contribute to a better understanding of a regularized relationship between the causative factors and landslide susceptibility. This makes it possible to incorporate knowledge-driven considerations of the causative factors into the landslide susceptibility analysis and also be extensively used to other areas.

Mineral Potential Assessment of Sedimentary Deposit using Frequency Ratio and Logistic Regression of Gangreung Area, Korea

Mineral resource potential mapping is an important procedure in mineral resource assessment. The aim of this study is to analyze relationships between sedimentary deposits and related factors and integrated the relationships using probabilistic and statistical models in GIS environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a potential map for deposits in the Gangreung area, Korea. This empirical approach assumes that all deposits share a common genesis and comprises three main steps such as identification of spatial relationships, quantification of identified spatial relationships and integration of multiple quantified spatial relationships. For this, a spatial database including sedimentary mineral deposit, topographic, geologic, geophysical and geochemical data were constructed for the study area using Geographic Information System (GIS). The used 55 sedimentary mineral deposits and the related to factors, geological data such as lithology and fault, geochemical data such as Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, W, Zn, Cl, F- , PO4 2-, NO2 -, NO3 - and SO4 2-, HCO3 -, pH, Eh, Conductivity, geophysical data such as Bouguer and magnetic anomaly were used. Using the constructed spatial database, the relationships between minerals deposit areas and related 36 factors were identified and quantified by frequency ratio and logistic regression models which are probabilistic and statistical model. All factors were used for mapping of regional mineral potential using overlay method in GIS environment. Then, the mineral potential map was verified using existing mineral deposit area. The verification results showed 89.53% and 92.83% in frequency ratio and logistic regression models each.

A case study for the integration of predictive mineral potential maps

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

Application of Artificial Neural Network for Mineral Potential Mapping

Mineral exploration is a multidisciplinary task requiring the simultaneous consideration of numerous disparate geophysical, geological, and geochemical datasets (Knox-Robinson, 2000). The size and complexity of regional exploration data available to geologist are increasing rapidly from a variety of sources such as remote sensing, airbone geophysics, large commercially available geological and geochemical data (Brown et al., 2000). This demands more effective integration and analysis of regional and various of geospatial data with different formats and attributes. In addition, this needs spatial modeling techniques using observations regarding the association of mineral occurrences with various geological features in a qualitative manner. Geographic Information System (GIS) methods are very useful for processing and combining data within maps in mineral potential mapping. The development of GIS-based methods for integration and analysis of regional exploration datasets has an important role in assisting the decision-making processes for geologists in selection of exploration area (Brown et al., 2000). More recently, the mineral exploration industry has taken this approach further and with the help of spatial data modeling in GIS (Partington, 2010). The spatial modeling techniques been proposed for mineral potential mapping, such as weights of evidence model (Bonham-Carter et al., 1988, 1989; Agterberg et al., 1990; Xu et al., 1992; Rencz et al., 1994; Pan, 1996; Raines, 1999; Carranza & Hale, 2000; Tangestani & Moore, 2001; Carranza, 2004; Agterberg & Bonham-Carter, 2005; Jianping et al., 2005; Nykanen & Raines, 2006; Porwal et al., 2006; Roy et al., 2006; Nykanen & Ojala, 2007; Raines et al., 2007; Oh & Lee, 2008; Harris et al., 2008; Benomar et al., 2009), Bayesian network classifiers (Porwal et al., 2006), logistic regression (Chung and Agterberg, 1980; Agterberg, 1988; Oh & Lee, 2008), fuzzy logic (An et al., 1991; Bonham-Carter, 1994; Eddy et al., 1995; D’Ercole et al., 2000; Knox-Robinson, 2000; Luo & Dimitrakopoulos, 2003; De Quadros et al., 2006; Carranza et al., 2008; Nykanen, 2008), artificial neural networks (Singer & Kouda, 1996; Harris & Pan, 1999; Brown et al., 2000, 2003; Rigol-Sanchez et al., 2003; Behnia, 2007; Skabar, 2007; Oh & Lee, 2008), and an evidence theory model (Moon, 1990, 1993; An & Moon, 1993; Moon & So, 1995; Porwal et al., 2003; Carranza et al., 2005). Researches using GIS have involved comparison of methods (Harris et al., 2003; Oh & Lee, 2008) and resolutions of spatial data used for mapping mineral potential, development of advanced methods,

Ground Subsidence Hazard Analysis in an Abandoned Underground Coal Mine Area using Probabisltic and Logistic Regression Models

For quantitative analysis of presumptive ground subsidence near Abandoned Underground Coal Mine (AUCM), this study applied, verified and compared a probability model, a frequency ratio and statistical model, a logistic regression at Simpori of Samcheok city in Korea, using a Geographic Information System (GIS). To evaluate susceptibility of the subsidence, database was constructed using topographical map, geological map, mining tunnel map, GPS data, land use map, lineaments, DEM and borehole data. Then, the relationship between the factors and the existing subsidence were calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient, were overlaid to determine each factors rating for ground subsidence susceptibility mapping. Then the ground subsidence susceptibility map was compared with known ground subsidence locations and verified. The verification results showed very high prediction accuracy (about 90%). The frequency ratio model (93.29%) had higher prediction accuracy than logistic regression (89.35%). The maps can be used to reduce hazards associated with ground subsidence and to land cover planning.