산지재해는 1차적으로 산지사면에서 산사태가 발생되어 2차적으로 계류를 따라 토석류로 이동 및 확산되면서 산지 하부지역의 시설지와 주거지에 피해를 발생시킨다. 따라서 본 연구는 전라북도 지역의 토석류 발생지 79개소를 조사 대상으로 현장조사를 통한 발생 길이에 영향을 미치는 인자를 구명하고, 수량화이론(I)을 이용하여 발생 길이에 대한 각 인자의 기여도 분석을 통해 예방적인 측면에서 전라북도 지역 내 토석류 발생 위험지역에 대한 예측기준을 작성하였다. 토석류의 발생 길이에 영향을 미치는 인자는 모암(화성암), 횡단사면(복합사면), 입목 흉고직경(6cm 이하), 표고(501m 이상), 발생위치(산록) 등이었다. 각 인자의 범위를 추정한 결과, 모암(0.5633)이 가장 높게 나타나 전라북도 지역의 토석류 발생 위험도에 큰 영향을 미치는 것으로 추정되었으며, 다음으로는 횡단사면(0.4565), 사면위치(0.3568), 흉고직경(0.3274), 표고(0.3052)순으로 나타났다. 전라북도 지역 산지에서 토석류 발생 위험도 판정식을 기준으로 5개 인자의 카테고리별 점수를 계산한 추정치 범위는 0점에서 2.0092점 사이에 분포하였다. 중앙값인 1.0046점을 기준으로 토석류 위험도 예측을 위한 등급을 분류한 결과 Ⅰ등급은 1.5070 이상, Ⅱ등급 1.0047 ∼ 1.5069, Ⅲ등급 0.5023 ∼ 1.0046, IV등급 0.5022 이하로 나타났고, Ⅰ등급과 Ⅱ등급에서 토석류 발생비율이 76%로서 비교적 높은 적중률을 보였다. 따라서 본 판정표는 전라북도의 산지에서 지역의 위험 비탈면에 있어서 토석류 발생 위험도 판정에 유용하게 활용할 수 있을 것으로 판단된다.

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It’s five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.

Korea`s industrial death rate is 13 percent in 2015. It’s five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won in natural disaster losses equivalent to 1.1 trillion won, requiring fundamental changes in industrial safety levels. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spacial analysis and decision tree methodologies. We draw the main factors that affect the accident and developed the four risk category(alert, hazard, caution, and attention). It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings, targeted education, and technical support.

The purpose of this study was to identify vulnerable area of emergency medical care. In the existing method, the emergency medical vulnerable area is set as an area that can not reach the emergency room within 30 minutes. In this study, we set up an area that can not reach within 30 minutes including the accessibility of 119 emergency center. To accomplish this, we obtained information on emergency room and 119 emergency center through Open API and constructed road network using digital map to perform accessibility analysis. As a result, 509 emergency room are located nationwide, 78.0% of them are concentrated in the region, 1,820 emergency center are located, and 61.0% of them are located in rural areas. The average access time from the center of the village to the emergency room was analyzed as 15.3 minutes, and the average access time considering the 119 emergency center was 21.8 minutes, 6.5 minutes more. As a result of considering the accessibility of 119 emergency center, vulnerable areas increased by 2.5 times, vulnerable population increased by 2.0 times, and calculating emergency medical care vulnerable areas, which account for more than 30% of the urban unit population, it was analyzed that it increased from 17 to 34 cities As a further study, it will be necessary to continuously monitor and research the real-time traffic information, medical personnel, medical field, and ambulance information to reflect the reality and to diagnose emergency medical care in the future.

The objective of this study was to make a map of farmland vulnerability to flood inundation based on morphologic characteristics from the flood-damaged areas. Vulnerability mapping based on the records of flood damages has been conducted in four successive steps; data preparation and preprocessing, identification of morphologic criteria, calculation of inundation vulnerability index using a fuzzy membership function, and evaluation of inundation vulnerability. At the first step, three primary digital data at 30-m resolution were produced as follows: digital elevation model, hill slopes map, and distance from water body map. Secondly zonal statistics were conducted from such three raster data to identify geomorphic features in common. Thirdly inundation vulnerability index was defined as the value of 0 to 1 by applying a fuzzy linear membership function to the accumulation of raster data reclassified as 1 for cells satisfying each geomorphic condition. Lastly inundation vulnerability was suggested to be divided into five stages by 0.25 interval i.e. extremely vulnerable, highly vulnerable, normally vulnerable, less vulnerable, and resilient. For a case study of the Jinju, farmlands of 138.6km2, about 18% of the whole area of Jinju, were classified as vulnerable to inundation, and about 6.6km2 of farmlands with elevation of below 19 m at sea water level, slope of below 3.5 degrees, and within 115 m distance from water body were exposed to extremely vulnerable to inundation. Comparatively Geumsan-myeon and Sabong-myeon were revealed as the most vulnerable to farmland inundation in the Jinju.