• Journal of the Korea Society of Computer and Information
• /
• v.26 no.5
• /
• pp.95-101
• /
• 2021

In this study, we present how to improve the current seismic disaster information service by utilizing Shake, which can express the effects of earthquakes in the form of isolines. Using ShakeMap software provided by the U.S. Geological Survey, an automated rapid ShakeMap generation system was implemented, and based on this, an earthquake disaster information service improvement model was presented to identify earthquake risk in the form of intensity or peak ground acceleration. In order to verify the feasibility and effectiveness of the improved model, the seismic disaster information service app. was developed and operated on a trial basis in Pohang, Gyeongsangbuk-do. As a result of the operation, it was found that more detailed seismic risk information could be provided by providing information using rapid ShakeMap to induce users' safety behavior more effectively.

• The Journal of the Korea Contents Association
• /
• v.17 no.1
• /
• pp.351-362
• /
• 2017

Recently, analysis techniques to extract new meanings using big data analysis and various services using them have been developed. A disaster safety service among such services has been paid attention as the most important service. In this paper, we design and implement a flood disaster safety system using real time weather big data. The proposed system retrieves and processes vast amounts of information being collected in real time. In addition, it analyzes risk factors by aggregating the collected real time and past data and then provides users with prediction information. The proposed system also provides users with the risk prediction information by processing real time data such as user messages and news, and by analyzing disaster risk factors such a typhoon and a flood. As a result, users can prepare for potential disaster safety risks through the proposed system.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.599-608
• /
• 2018

As natural disasters are increasing due to the unusual weather and the modern society is getting complicated, the rapid change of the urban environment has increased human disasters. Thus, citizens are becoming more anxious about social safety. The importance of preparation for safety has been suggested by providing the disaster safety services such as regional safety index, life safety map, and disaster safety portal application. In this paper, we propose an application framework to predict the urban safety index based on user's location with realtime weather/atmosphere data after creating a predication model based on the machine learning using number of occurrence cases and weather/atmosphere history data. Also, we implement an application to provide traffic safety index with executing preprocessing occurrence cases of traffic and weather/atmosphere data. The existing regional safety index, which is displayed on the Si-gun-gu area, has been mainly utilized to establish safety plans for districts vulnerable to national policies on safety. The proposed system has an advantage to service useful information to citizens by providing urban safety index based on location of interests and current position with realtime related data.

• Journal of the Society of Disaster Information
• /
• v.16 no.4
• /
• pp.682-690
• /
• 2020

Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.1
• /
• pp.91-100
• /
• 2011

Fire stations should be located in optimal location to act quickly in case of emergency and minimize damages as a public facility that protects life and property of local residents. Siting fire stations without regard for the accessibility and occurrence factors of an accident may provide fire service unequally. Therefore service analysis is necessary to reduce the blind spot of disaster and safety and to offer equal fire-service at this time. Especially fire stations can service more efficiently than before by reducing a more likely vulnerable area. This study suggests methods of mapping index of value that represents vulnerability of the occurrence of an accident and extracting service zone map of fire stations, as an initial research for offering efficient fire service. Geographical distribution mismatch between service zone map and vulnerability map is figured out and vulnerable area for fire service is defined by using map overlay method.

• Journal of Cadastre & Land InformatiX
• /
• v.45 no.2
• /
• pp.87-100
• /
• 2015

Currently, Korea government has strongly recommended the storm and flood insurance system to reduce the damage caused by natural disasters. The storm and flood insurance operated by private insurance company is the type of policy insurance. and is supervised by Minister of Public Safety and Security. It is the advanced disaster management system which is able to protect the public interests through unexpected natural disaster by assisting some part of the insurance premium from a central or local government. The main purpose of the present investigation is to build the insurance object GIS DB which should be necessary to calculate the premium rate in the map for storm and flood insurance, and also, to perform GIS analysis. The service model in this study is aimed to general single house, apartment and green house. The service management plan targeting the whole country has been investigated in terms of building DB and service operation.

• The Journal of the Korea Contents Association
• /
• v.10 no.2
• /
• pp.81-88
• /
• 2010

This paper describes a development of next generation information remote retrieval and warning system that enables the user to make slope information retrieval remotely for a rockfall and landslide disaster reduction from mobile environments. And this system will be able to warn with a real-time stability condition about the slope which circumference are contiguous in standard user location. Slope information which provides to the user, become the service which upgrades from depth deep information directness will be able to confirm in order from field with applies multimedia style information which is various. In order to retrieve slope information with the wire and wireless internet from the remote place, we used mobile PC carrying is simple. Also this system attached GPS receiver to mobile PC in order to confirm user location as a real-time from the electronic map from field. Specially this system user location divide the safety of the slope which within the area where are fixed in the center are representative with 'safe area', 'collapse area' and 'collapse forecast area' etc. And to indicate with the icon of each other different color simultaneously in the electronic map. With like that reason, this system which sees the user even while moving safety condition about circumferential slope from the electronic map is having the strong point will be able to grasp with a real-time in one eye. Also warning message leads at the case real-time when the collapse will occur in specific slope, to inform to the user. Therefore this system which sees will be able to reduce the disaster which is caused by in landslide a very big strong point and has.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.12
• /
• pp.85-97
• /
• 2021

In this paper, in order to support rapid and effective decision-making and response in disaster situations, we identified the author's organization of academic research papers and conducted a collaborative relationship analysis study based on this. For this purpose, 2,308 papers in 69 Korean academic journals classified by disaster and safety type were selected for analysis and experimental data were constructed based on the Korea Science Citation Database (KSCD) and institutional identification data provided by KISTI. Collaborative relationship analysis was conducted for each of the four units (Institution, Institution type, Institution region and University department type). First, statistical status such as frequency of appearance was compared, and basic properties and main centrality index of each co-occurrence network were calculated and analyzed using Social Network Analysis Method. In addition, a visualization map was created and presented for each network so that the collaborative relationship could be viewed and understood as a whole. The results of this study are expected to contribute to the search activities of institutions and cooperative groups that support effective disaster response and to lay the foundation for the information service system.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_4
• /
• pp.1179-1194
• /
• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Fire Science and Engineering
• /
• v.33 no.4
• /
• pp.140-151
• /
• 2019

It is to assess the indoor air quality of the chemical hazardous materials exposed to the fire after firefighters returned to the fire scene. The research subject randomly selected four fire stations located in Seoul, Korea. Two fire stations were set up as control groups after the return of the firefighting activities at the actual fire scene, and two other fire stations were set up as control groups to measure the air quality of the room at normal levels regardless of the action. We conducted 24-hour monitoring for all fire accidents that occurred in Seoul Metropolitan using fire safety map computer system. Also, indoor air quality was measured immediately after homecoming if the experiment group was to be dispatched due to an accident of intermediate or larger scale. 11 hazardous substance items such as fine dust, formaldehyde, volatile organic compounds, PAH, VCM, acidity, asbestos, CO₂, NO₂, O₃ were measured according to the process test method. Three of 11 types of harmful substances exceeded domestic and foreign standards, and one of them was found to be close to foreign standards. In particular, total volatile organic compounds, carbon dioxide and sulfuric acids were 2.5 times, 2.2 times and 1.1 times higher than the standard. Also, for formaldehyde and sulfuric acid, it was measured higher in the control group than in the case group. This findings could be used in policies to improve indoor air quality in the fire station of the Seoul Metropolitan Government.