As a city develops and expands, it is likely confronted with a variety of environmental problems. Although the impact of climate change on people has continuously increased in the past, great numbers of natural disasters in urban areas have become varied in terms of form. Among these urban disasters, urban flooding is the most frequent type, and this study focuses on urban flooding. In cities, the population and major facilities are concentrated, and to examine flooding issues in these urban areas, different levels of flooding risk are classified on 100 m × 100 m geographic grids to maximize the spatial efficiency during the flooding events and to minimize the following flooding damage. In this analysis, vulnerability and exposure tests are adopted to analyze urban flooding risks. The first method is based on land-use planning, and the building-to-land ratio. Using fuzzy approaches, the tests focus on risks. However, the latter method using the HEC-Ras model examines factors such as topology and precipitation volume. By mapping the classification of land-use and flooding, the risk of urban flooding is evaluated by grade-scales: green, yellow, orange, and red zones. There are two key findings and theoretical contributions of this study. First, the areas with a high flood risk are mainly restricted to central commercial areas where the main urban functions are concentrated. Additionally, the development density and urbanization are relatively high in these areas, in addition to the old center of urban areas. In the case of Changwon City, Euichang-gu and Seongsan-gu have increased the flood risk because of the high property value of commercial areas and high building density in these regions. Thus, land-use planning of these districts should be designed to reflect upon the different levels of flood risks, in addition to the preparation of anti-disaster facilities to mitigate flood damages in high flood risk areas. Urban flood risk analysis for individual land use districts would facilitate urban planners and managers to prioritize the areas with a high flood risk and to prepare responding preventive measures for more efficient flood management.

In this study, focusing on buildings as the smallest unit of urban space, the distribution characteristics of risk factors were examined by building use as an adaptable measure for urban flooding disasters. Flood risk is calculated as a function of hazard, exposure, and vulnerability. The flood risk for a building was classified into five classes, and the distribution characteristics of buildings were examined according to England’s flood risk vulnerability classification system, known as Planning Policy Statement 25 (PPS25). After analyzing the risk of flooding in Ulsan Metropolitan City, one of Korea’s representative urban areas, it was found that while Dong-gu District can be considered relatively safe, districts of Jung-gu and Nam-gu, as well as Ulju-gun, have highly vulnerable buildings with red and orange ratings, which include motor vehicles-related facilities, education and welfare facilities, and residential facilities. There has been evidence to prove that urban flood disaster affects topography and the environment, in addition to having a significant effect on adaptability depending on the facility groups that resulted from urbanization. This study is expected to serve as a scientific database for disaster risk reduction and climate change adaptation to floods during land-use planning, which would eventually allow for systematic management of high-risk buildings through verification of location suitability of buildings by facility group.

1. 서 론 기후변화로 홍수의 가능성과 피해의 불확실성은 증가가 고 있다. 홍수는 직접적인 인명피해 외에도

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

본 연구는 수업개선을 위한 대학교수들의 수업설계 수행 정도가 어떠한지를 확인함과 동시에 경력 발달의 과정(수업에 관한 관심이 어디 있느냐를 토대