• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1173-1181
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• 2020

Recently, as local heavy rains occur frequently in a short period of time, economic and social impacts are increasing beyond the simple primary damage. In advanced meteorologically advanced countries, realistic and reliable impact forecasts are conducted by analyzing socio-economic impacts, not information transmission as simple weather forecasts. In this paper, the degree of flooding was derived using the Spatial Runoff Assessment Tool (S-RAT) and FLO-2D models to calculate the threshold rainfall that can affect human walking, and the threshold rainfall of the concept of Grid to Grid (G2G) was calculated. In addition, although it was used a lot in the medical field in the past, a quantitative accuracy analysis was performed through the ROC analysis technique, which is widely used in natural phenomena such as drought or flood and machine learning. As a result of the analysis, the results of the time period similar to that of the actual and simulated immersion were obtained, and as a result of the ROC (Receiver Operating Characteristic) curve, the adequacy of the fair stage was secured with more than 0.7.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.137-143
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• 2013

The high resolution satellite images are used in many fields such as weather observation, remote sensing, military facilities monitoring, cultural properties protection etc. Although satellite images are obtained in same satellite imaging system, the satellite images are degraded depending on the condition of hardware(optical device, satellite operation altitude, image sensor, etc.). Due to the fact that changing the hardware of satellite imaging system is impossible for resolution enhancement of these degraded satellite after launching a satellite, therefore the method of resolution enhancement with satellite images is necessary. In this paper the resolution is enhances by using a Super Resolution(SR) algorithm. The SR algorithm is an algorithm to enhance the resolution of an image by uniting many low resolution images, so an output image has higher resolution than using other interpolation methods. But It is difficult to obtain many images of the same area. Therefore, to solve this problem, we applied SR after by applying the affine and projection transform. As a results, we found that the images applied SR after affine and projection transform have higher resolution than the images only applied SR.

• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.469-478
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• 2019

Purpose: In this studies we examine the facilities damage characteristics caused by forest fire. Therefore, we surveyed damaged facilities from forest fire which was occurred on Goseong-Gun on march 28 in 2019.(damaged areas was 40ha) The types of facilities uses were house, public facility, warehouse and so on. 17 facilities were destroyed. The purpose of this study was to for establishing a disaster safety village in rural areas where damage from a similar type of disaster occurs repeatedly by conducting the consciousness survey targeting at experts and disaster safety officials in a local government. Method: We surveyed meteorological factors(temperature, wind speed, wind direction, humidity) per a minute for analyzing weather condition on Goseong-Gun when forest fire was occurred, spread and extinguished. And we surveyed forest fire risk factors(a slope degree, a slope direction, a geographical feature, a distance between forest and facility, main species, the existence of crown fire ignition, the direction of facility, the main material of building) around 10 damaged facilities. Finally, we analyzed damage pattern of facilities using meteorological factor and forest fire reisk fator Result: The weather condition of Kanseonng AWS (No.517) was high temperature, arid and strong wind, when the forest fire was occurred and spread. An average wind speed was 4.1m/s and the maximum wind speed was 11.6m/s. The main direction of wind was W(225~315°). Damaged facilities were located on the steep slope area and on the mountaintop. The forest density around facilities was high and main species was korean red pine. The crown fire was occurred in the forest around damaged facilities. The average distance was 13.5m from forest to facilities. When the main matarial of building was made by fire resistance materials (for example, rainforced concrete), the damage was slightly. on the other hand, when by flammable material (for example, a Sandwich Panel), the facilities were totally destroyed Conclusion: The results of this research which were the thinning around house, making a safety distance, the improvement of main material of building and etc, will be helpful for establishing a counter measure for a forest fire prevention of facilities in wild land urban interface

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1355-1362
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• 2016

Recently, the marine leisure are being emphasized with improving the quality of life style by increased income and spare time. Also, there is a increasement of people's interest in marine leisure activities. But resources and facilities do not grow in proportion to the quantitative growth of the current marine leisure industry. Besides, a leisure ship operator tends to choose a simple or familiar route of the local area rather than a new leisure routes which are not explored due to lack of accessible areas information. This paper proposes a routes recommendation system in order to solve above problems based on marine resource database. The databases have been constructed through investigation and analysis of navigational information such as environmental conditions including weather conditions and sea status, field of marine leisure activities, tourist attractions and natural landscape, and marine leisure prohibited areas. Therefore we have developed and implemented the route recommendation system that provides various information necessary to route operation of leisure boats.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.63-70
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• 2011

A bicycle is different from vehicles in the structure that a rider is fully exposed to the surrounding environment. Therefore, it needs to make use of prior information about local weather, air quality, trail road condition. Moreover, since it depends on human power for moving, it should acquire route property such as hill slope, winding, and road surface to improve its efficiency in everyday use. Recent mobile applications which are to be used during bicycle riding let us aware of the necessity of development of intelligent bicycles. This study aims to develop a riding state (up-hill, down-hill, accelerating, braking) recognition algorithm using a low-power wrist watch type embedded system which has 3-axis accelerometer and wireless communication capability. The developed algorithm was applied to 19 experimental riding data and showed more than 95% of correct recognition over 83.3% of the total dataset. The altitude and temperature sensor also in the embedded system mounted on the bicycle is being used to improve the accuracy of the algorithm. The developed riding state recognition algorithm is expected to be a platform technology for intelligent bicycle interface system.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.99-105
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• 2014

Since traffic accidents account for the highest proportion of the artificial disasters which occur in urban areas along with fire, more scientific an analysis on the causes of traffic accidents and various prevention measures against traffic accidents are needed. In this study, the research selected Jinju-si, which belongs to local small and medium-sized cities as a research target to analyze the characteristics of temporal and spacial distribution of traffic accidents by associating the data of traffic accidents, occurred in 2013 with the causes of traffic accidents and location information that includes occurrence time and seasonal features. It subsequently examines the spatial correlation between traffic accidents and the characteristics of urban space development according to the plans of land using. As a result, the characteristics of accident distribution according to the types of accidents reveal that side right-angle collisions (car versus car) and pedestrian-crossing accident (car versus man) showed the highest clustering in the density analysis and average nearest neighbor analysis. In particular, traffic accidents occurred the most on roads which connect urban central commercial areas, high-density residential areas, and industrial areas. In addition, human damage in damage conditions, clear day in weather condition, dry condition in the road condition, and three-way intersection in the road way showed the highest clustering.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.69-75
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• 2015

Road facilities for safe driving were designed for drivers to distinguish them during day and night, but they cannot play their role when the weather becomes worse. Recently, the road facilities have been designed by using electric and electronic technology so that they can be displayed well at a long distance, but they should be replaced very often due to their frequent breakdown. So, there are many problems in traffic calming and maintenance. In this study, to solve the above problems, semi-permanent LED beacon light was installed in the area where traffic accident are frequent, and monitoring system was developed so that the LED beacon light can be maintenanced by connecting with system. For the above installation and development, system was based on window operating system and it was developed for worker to operate it by using P.C. through connecting with wireless local area network. The result of this study led to analyzing state information on the battery of field-installed LED beacon light in real time, and manegement to effectively by predicting their life cycle.