• 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 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.

• 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.

• Economic and Environmental Geology
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• v.33 no.6
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• pp.557-563
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• 2000

Subsidence hazard has never been considered seriously until recent yews in Korea, although its socioeconomic impact on Korea becomes more and more enormous. There have been a few studies for the application of GIS analysis technique to the prediction of subsidence hazard. For GIS analysis, several factors, which are represented by coverage, are considered and selected for building a GIS model. Numerical method was used to quantify the importance of each factor in GIS model and the result from numerical modeling using FLAC was compared with that from previous research based on empirical methods. Analysis in 3-D needs more computer resources (i.e. memory). Therefore that in 2.5-D was considered to overcome the problem. Not only maximum vertical subsidence but also maximum horizontal strain and maximum slope have been considered for the assessment of subsidence hazard. The model can be easily modified for the purpose of applications in any subsidence area, especially cavern or abandoned mines under thick soil layer.

• Journal of the Korean Geosynthetics Society
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• v.12 no.2
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• pp.67-74
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• 2013

In this study, the prediction of slope hazard probability was performed to the study area located in Hadae-ri, Woochun-myeon, Hoengsung-gun, Gangwon Province around Youngdong express way using the computer program SHAPP ver 1.0 developed by a decision tree model. The soil samples were collected at total 10 points, and soil tests were performed to measure soil properties. The thematic maps of soil properties such as coefficient of permeability and void ratio were made on the basis of soil test results. The slope angle analysis of topography was performed using a digital map. As the prediction result of slope hazard probability, 2,120 cells among total 27,776 cells were predicted to be in the event of slope hazards. Therefore, the predicted area of occurring slope hazards may be $53,000m^2$ because the analyzed cell size was $5m{\times}5m$.

• 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.

• Disaster Prevention Review
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• v.14 no.4
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• pp.124-125
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• 2012

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.75-80
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• 2011

The main objective of this research is to develop fatigue prediction model for flexible pavements using energy ratio factor and phase angle. The two parameters are considered as fundamental properties of time and temperature dependent viscoelastic asphalt concrete materials. The energy ratio factor is defined as the ratio of the pseudo-total cumulative dissipated energy to the cumulative dissipated energy to failure during the test. The phase angle between the stress and strain ware signals stems from the intrinsic the dependent asphalt mixture behavior. The phase angle was computed and the relationship between the initial mixture stiffness and the initial phase angle is presented. As a result, fatigue prediction model for flexible pavements was proposed using intrinsic properties of viscoelastic asphalt concrete materials.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.297-298
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• 2021

The construction industry is a representative high levels of hazardous work environment, and the number of disasters in the construction industry is the highest compared to other industries. Information & Communication Technology(ICT) convergence technologies are being introduced worldwide, and this study considers ICT-based safety management cases at domestic and foreign, resulting in a list of technologies to recognize and resolve to construction site hazards. Technologies such as 「Development of hazard map considering cause, age, proficiency, type and timing of accident in construction industry」, 「Development of hazard map-based accident prediction platform using AI」, 「Development of smart safety management plan for whole construction work cycle」, 「Development of intelligent safety devices and smart safety equipment for mitigating hazard factors」 were derived as smart construction technologies that could solve this problem.