• International conference on construction engineering and project management
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• 2015.10a
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• pp.671-672
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• 2015

Due to harsh conditions of disaster areas, understanding of current feature of collapsed buildings, terrain, and other infrastructures is critical issue for disaster managers. However, because of difficulties in acquiring the geographical information of the disaster site such as large disaster site and limited capability of rescue workers, comprehensive site investigation of current location of survivors buried under the remains of the building is not an easy task for disaster managers. To overcome these circumstances of disaster site, this study makes use of an unmanned aerial vehicle, commonly known as a drone to effectively acquire current image data from the large disaster areas. The framework of 3D model reconstruction of disaster site using aerial imagery acquired by drones was also presented. The proposed methodology is expected to assist rescue workers and disaster managers in achieving a rapid and accurate identification of survivors under the collapsed building.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Disaster Prevention Review
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• v.17 no.2
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• pp.14-33
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• 2015

• Journal of the Society of Disaster Information
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• v.10 no.2
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• pp.304-311
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• 2014

Disaster Resources are the resources used in the disaster fields such as fire, typhoon, flood, collapse, flood damage, distress, relief, sanitary, explosion and so forth. The resources are distributed by the several institutes including governmental agencies, public institution, and private sectors. When the large scale disaster is occurred, many kinds of the disaster resources are needed. But it is not easy to know where institutes has the needed resources, because the resource management systems are all distributed in the each institute. To integrate or connect the resource data among the systems, it need the several kinds of coordinations such as terms, classification, and resource exchange protocols. In this paper, we propose the standard specifications and how to connect and exchange the data among the systems.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.171-182
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• 2022

In this study, by classifying latent groups for disaster safety awareness focusing on the four sub-factors of the developed disaster awareness scale of high school students, the characteristics of each group were examined, and the differences between latent classes according to inter-individual differences were investigated. As a result of analysis based on the data of a total of 1054 high school students, the disaster safety awareness of high school students was classified into three latent groups. Each latent group was named 'High Safety Awareness Type(SAT)', 'Normal SAT', and 'Low SAT' according to its characteristics. In all four fire safety awareness sub-factors, 'High SAT', which had a high score, accounted for 56.5% of the total, and 'Normal SAT', which had a moderate score in the sub-factors, had the lowest ratio at 20.3%. There were no significant differences by gender, grade, and academic achievement of the latent group. These results are not only meaningful as the first study of the latent profile analysis of high school students on disaster safety awareness, but also help to identify the characteristics of individuals in each latent group with more subdivisions and provide useful data for disaster safety awareness education according to individual differences. The implications of this study and suggestions for follow-up studies were discussed.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.933-939
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• 2011

Recent heavy rains have caused natural disasters such as flooding and landslides nationwide. Because of flooding occurrence in most of the roads, traffic congestion and isolation caused many loss especially at rush hour. Constant monitoring and analysis of past disaster history data are needed to prevent disasters on areas prone to floods and disaster risk areas. If we managed to obtain traffic volume, speed, phase around intersection using disaster history data when disasters occurred, we can analyse traffic congestion, change of disaster scale and rainfall. In this study, We select a target district to develop by using a route from Dae-nam intersection in Busan Namgu Daeyoeon-dong, over Gwangan large bridge up until Haeundae Olympic intersection, We developed a system which searches disaster history information, traffic volume using disaster history data based on user selection of the road.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.61-67
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• 2017

In order to establish and operate a rapid and effective disaster safety management system in an emergency situation that threatens the safety of citizens, such as disaster, accident or terrorism, appropriate responses are necessary. An integrated task execution system for rapid response and restoration should be implemented not only by the central ministries related to disaster management and response, but also by local governments, NGO, and individuals, under clear role sharing. In the case of Seoul city, it is urgent to establish an effective disaster management system for preventing and responding to disasters, because of the increasing possibility of natural disasters due to climate change, the threat of terrorism, urban decay and the industrial accidents. From the perspective of governance, this study tried to seek out countermeasures such as disaster response system and command system at disaster site centering on Seoul city government interdepartmental organization system, implementation process and systematization of response procedures.

• Disaster Prevention Review
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• v.14 no.3
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• pp.108-109
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• 2012

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.223-236
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• 2020

Purpose: In this study, a strong wind damage prediction function was developed in order to be used as a contingency during disaster management (preventive-preventive-response-recovery). Method: The predicted strong wind damage function proposed in this study took into account the re-enactment boy power, weather data and local characteristics at the time of damage. The meteorological data utilized the wind speed, temperature, and damage history observed by the Korea Meteorological Administration, the disaster year, and the recovery costs, population, vinyl house area, and farm water contained in the disaster report as factors to reflect the regional characteristics. Result: The function developed in this study reflected the predicted weather factors and local characteristics based on the history of strong wind damage in the past, and the extent of damage can be predicted in a short time. Conclusion: Strong wind damage prediction functions developed in this study are believed to be available for effective disaster management, such as decision making by policy-makers, deployment of emergency personnel and disaster prevention resources.

• Research in Community and Public Health Nursing
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• v.30 no.2
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• pp.217-225
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• 2019

Purpose: The purpose of this study is to assess socio-demographic, disaster-related, physical health-related, psychological, and social factors that may adversely affect disaster victims' QoL (Quality of Life). Methods: A cross sectional study was designed by using the secondary data. From the 3rd Disaster Victims Panel Survey (2012~2017), a total of 1,659 data were analyzed by using descriptive statistics including frequency, percentage, t-test, ANOVA, and multivariate linear regression. Results: Older people with lower health status lacking financial resources prior to a disaster were more at risk of low levels of QoL. Lower levels of perceived health status, resilience, and QoL were reported by disaster exposed individuals, while their depression was higher than the depression in the control group of disaster unexposed ones. Resilience, social and material supports were positively associated with QoL whereas depression and PTSD (Post-Traumatic Stress Disorders) were negatively associated. Conclusion: These findings suggest that psychological symptoms and loss due to disasters can have adverse impacts on the QoL of disaster victims in accordance with their prior socio-demographic background. They also indicate that targeted post-disaster community nursing intervention should be considered a means of increased social support as well as physical and mental health care for disaster victims.