• 환경영향평가
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• 제15권6호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.25-29
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• 2006

Noise map presents new alternatives of noise reduction counter measure and becomes important tool for making environmental policy. Many input factors such as road conditions, number of vehicles, speeds of vehicles are used for noise prediction of the noise map. However, results of noise prediction make difference depending on the values of the input factors. In this study, effect of measurement method for vehicle speeds all the prediction results of the noise map.

• 한국소음진동공학회논문집
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• 제17권2호
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• pp.155-159
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• 2007

Noise map presents new alternatives of noise reduction counter measure and becomes important tool for making environmental policy. Many input factors such as road conditions, number of vehicles, speeds of vehicles are used for noise prediction of the noise map. However, results of noise prediction make difference depending on the values of the input factors. In this study, it researched effect of measurement method for vehicle speeds on the prediction results of the noise map.

• 한국소음진동공학회논문집
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• 제20권6호
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• pp.515-520
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• 2010

The noise map has been applied to predicting the effect of noise and establishing the noise abatement measure for several years overseas. However the introduction of the noise map in Korea is at the initial stage. Thus, we surveyed the several prediction models for road traffic noise used in EU, and the method of applying the noise map in noise impact assessment. In order to improve the noise impact assessment we have to apply the noise map, and propose the guideline of predicting the road traffic noise. We intend to obtain coherency and accuracy of prediction results. As a result of this study, we know that the prediction guideline is an essential prerequisite in order to predict the unified and accurate road traffic noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.867-871
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• 2007

Noise map is very efficient tool to evaluate the road traffic noise. However, calculation time and accuracy of noise map heavily depend on the grid size of noise map. In this study, effects of grid size on the prediction results of road traffic noise map have been investigated in detail for urban and rural areas, respectively, and efficient grid size for the noise map has been proposed.

• 한국소음진동공학회논문집
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• 제20권2호
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• pp.199-204
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• 2010

Noise map is very efficient tool to evaluate the road traffic noise. However, calculation time and accuracy of noise map heavily depend on the grid size of noise map. In this study, effects of grid size on the prediction results of road traffic noise map have been investigated in detail for urban and rural areas, respectively, and efficient grid size for the noise map has been proposed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.882-888
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• 2008

The increase in logistics and the emergence of high speed vehicles are aggravating the noise problem, and disturbing large numbers of people. Therefore, growing attention is being paid to the creation and utilization of a noise map in order to grasp and predict the influence of noise, and to establish a policy that effectively reduces noise. However, so far there has been little progress in developing a predictive formula for railway noise, and a noise prediction program suitable for the domestic environment. Therefore, there is a need for a method to apply the overseas prediction formula to domestic railway vehicles. This study evaluated the prediction method, the result of which is similar to that of a real measurement, using a noise map. It also employed a commercial software program, SoundPlan, to predict noise.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1378-1385
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• 2008

The probability of landslide hazards was predicted to natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analysis results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated, and then the landslides prediction map was made up by use of prediction model considering the effect factors. The susceptibility of stone relics induced by landslides was investigated as the grading classification of occurrence probability using the landslides prediction map. In the landslides prediction map, the high probability area of landslides over 70% of occurrence probability was 3,489m3, which was 10.1% of total prediction area. If landslides are occurred at the high elevation area, the three stories stone pagoda of Jinjeon-saji (National treasure No.122) and the stone lantern of Jinjeon-saji (Treasure No.439) will be collapsed by debris flow.

• 한국소음진동공학회논문집
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• 제23권4호
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• pp.295-300
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• 2013

The noise map can be applied to predict the effect of noise and establish the noise reduction measure. But the predicted value in the noise map can vary depending on the input variables. Thus, we surveyed the several prediction models and analyzed the changes corresponding to the variables for obtaining the coherency and accuracy of prediction results. As a result, we know that the Schall03 and CRN model can be applied to predict the railway noise in Korea and the correction value, such as bridges correction, multiple reflection correction, curve correction must be used for reflecting the condition of the prediction site. Also, we know that the prediction guideline is an essential prerequisite in order to obtain the unified and accurate predicted value for railway noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.872-876
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• 2007

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.