• Journal of the Korean GEO-environmental Society
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• v.17 no.6
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• pp.13-21
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• 2016

Since the landslide hazard areas prediction was analyzed by slope-angle and soil properties, regional characteristics is not taken. Therefore, in order to make more rational prediction, it is necessary to consider the characteristics of the region. Tree roots have been known to increase soil cohesion in landslide hazard areas and to vary the degrees depending on the tree type. In addition, a reasonable prediction of landslide hazard areas can be made by considering crown density based on crown distribution patterns of the area of interest. In this study, using the roots cohesion considering the crown density of the trees, which is in the landslides risk areas around Mt. Gwehwa in Sejong City, the landslides risk areas were predicted and compared with predicted results obtained by not considering root cohesion.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.9-9
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• 2000

For the planning of future land use for economic activities, an essential component is the identification of the vulnerable areas for natural hazard and environmental impacts from the activities. Also, exploration for mineral and energy resources is carried out by a step by step approach. At each step, a selection of the target area for the next exploration strategy is made based on all the data harnessed from the previous steps. The uncertainty of the selected target area containing undiscovered resources is a critical factor for estimating the exploration risk. We have developed not only spatial prediction models based on adapted artificial intelligence techniques to predict target and vulnerable areas but also validation techniques to estimate the uncertainties associated with the predictions. The prediction models will assist the scientists and decision-makers to make two critical decisions: (i) of the selections of the target or vulnerable areas, and (ii) of estimating the risks associated with the selections.

• Journal of the Korean GEO-environmental Society
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• v.20 no.6
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• pp.15-22
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• 2019

In this study, the hazard areas are identified by using the Newmark displacement model, which is a predictive model for identifying the areas at risk of landslide triggered by earthquakes, based on the results of field survey and laboratory test, and literature data. The Newmark displacement model mainly utilizes earthquake and slope related data, and the safety of slope stability derived from LSMAP, which is a landslide prediction program. Backyang Mt. in Busan where the landslide has already occurred, was chosen as the study area of this research. As a result of this study, the area of landslide prone zone identified by using the Newmark displacement model without earthquake factor is about 1.15 times larger than that identified by using LSMAP.

• Journal of the Korean GEO-environmental Society
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• v.18 no.12
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• pp.47-57
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• 2017

In this study, a predictive analysis was conducted on sediment disaster hazard area by selecting six research areas (Chuncheon, Seongnam, Sejong, Daejeon, Miryang and Busan) among the urban sediment disaster preliminary focus management area. The models that were used in the analysis were the existing models (SINMAP and TRIGRS) that are commonly used in predicting sediment disasters as well as the program developed through this study (LSMAP). A comparative analysis was carried out on the results as a means to review the applicability of the developed model. The parameters used in the predictions of sediment disaster hazard area were largely classified into topographic, soil, forest physiognomy and rainfall characteristics. A predictive analysis was carried out using each of the models, and it was found that the analysis using SINMAP, compared to LSMAP and TRIGRS, resulted in a prediction of a wider hazard zone. These results are considered to be due to the difference in analysis parameters applied to each model. In addition, a comparison between LSMAP, where the forest physiognomy characteristics were taken into account, and TRIGRS showed that similar tendencies were observed within a range of -0.04~2.72% for the predicted hazard area. This suggests that the forest physiognomy characteristics of mountain areas have diverse impacts on the stability of slopes, and serve as an important parameter in predicting sediment disaster hazard area.

• Earthquakes and Structures
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• v.10 no.2
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• pp.389-408
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• 2016

Recently, seismic hazard analysis has become a very significant issue. New systems and available data have been also developed that could help scientists to explain the earthquakes phenomena and its physics. Scientists have begun to accept the role of uncertainty in earthquake issues and seismic hazard analysis. However, handling the existing uncertainty is still an important problem and lack of data causes difficulties in precisely quantifying uncertainty. Ground Motion Prediction Equation (GMPE) values are usually obtained in a statistical method: regression analysis. Each of these GMPEs uses the preliminary data of the selected earthquake. In this paper, a new fuzzy method was proposed to select suitable GMPE at every intensity (earthquake magnitude) and distance (site distance to fault) according to preliminary data aggregation in their area using ${\alpha}$ cut. The results showed that the use of this method as a GMPE could make a significant difference in probabilistic seismic hazard analysis (PSHA) results instead of selecting one equation or using logic tree. Also, a practical example of this new method was described in Iran as one of the world's earthquake-prone areas.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.54-66
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• 2006

Korea Forest Service made the landslide hazard map for all mountainous districts over the country in May 2005. In this study, we selected landslide areas occurred in Jeonbuk from 02 August 2005 to 03 August 2005 as the study area. We extracted landslide areas using images taken by PKNU 3 System, which was developed by PE&RS Laboratory in Dept. of Satellite Information Sciences, Pukyong National University and verified the accuracy of landslide hazard map by overlaying landslide hazard areas extracted by PKNU 3 images. And we analyzed characteristics of an altitude, a gradient, an inclined direction, a flow length, a flow accumulation for landslide areas using mountainous terrain analysis and Stream Network analysis of ArvView 3.3. As a result of this study, it is necessary to adjust the unitage(%) by the class and to modify and improve the score table for prediction of landslide-susceptible area forming the foundation of making the landslide hazard maps.

• Journal of the Korean GEO-environmental Society
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• v.18 no.6
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• pp.21-30
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• 2017

This study analyzed the ground characteristics region by designating 3 research areas, Anrim-dong in Chungju City, Busa-dong in Daejeon Metropolitan City and Sinan-dong in Andong City out of the areas subject to concentrated management to prepare for sediment disaster in downtown areas. The correlation between ground characteristics were observed by using characteristics (crown density, root cohesion, rainfall characteristics, soil characteristics) and the risk areas were predicted through sediment disaster prediction modeling. Landslide MAPping (LSMAP), Stability Index MAPping (SINMAP) and Landslide Hazard MAP (LHMAP) were used for the comparative analysis of the hazard prediction model for sediment disaster. As a result of predicting the sediment disaster danger, in case of SINMAP which was generally used, excessive range was predicted as a hazardous area and in case of the Korea Forest Service's landslide hazard map (LHMAP), the smallest prediction area was assessed. LSMAP predicted a medium range of SINMAP and LHMAP as hazardous area. The difference of the prediction results is that the analysis parameters of LSMAP is more diverse and engineering than two comparative models, and it is found that more precise prediction is possible.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of the Korean Geosynthetics Society
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• v.16 no.3
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• pp.51-62
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• 2017

For the purpose of the study, of the 76 areas subject to preliminary concentrated management on sediment disaster in the downtown area, 9 areas were selected as research areas. They were classified into three stratified rock areas (Gyeongsan City, Goheung-gun and Daegu Metropolitan City), three igneous rock areas (Daejeon City, Sejong Special Self-Governing City and Wonju City) and three metamorphic rock areas (Namyangju City, Uiwang City and Inje District) according to the characteristics of the bedrock in the research areas. As for the 9 areas, analyses were conducted based on tests required to calculate soil characteristics, a predictive model for root adhesive power, loading of trees and on-the-spot research. As for a rainfall scenario (rainfall intensity), the probability of rainfall was applied as offered by APEC Climate Center (APCC) in Busan. As for the prediction of landslide risks in the 9 areas, TRIGRS and LSMAP were applied. As a result of TRIGRIS prediction, the risk rate was recorded 30.45% in stratified rock areas, 41.03% in igneous rock areas and 45.04% in metamorphic rock areas on average. As a result of LSMAP prediction based on root cohesion and the weight of trees according to crown density, it turned out to a 1.34% risk rate in the stratified rock areas, 2.76% in the igneous rock areas and 1.64% in the metamorphic rock areas. Analysis through LSMAP was considered to be relatively local predictive rather than analysis using TRIGRS.