• Journal of the Korean earth science society
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• v.34 no.7
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• pp.605-614
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• 2013

Most steep slope failures tend to take place in geographically unstable areas. Mt. Baekdusan is known as a potentially active volcano in a typical mountainous terrain. This study prepared a digital elevation model of Mt. Baekdusan area and created a hazard map based on topographical factors and structural lineament analysis. Factors used in vulnerability analysis included geographical data involving aspect and slope distribution, as well as contributory area of upslope, tangential gradient curvature, profile gradient curvature, and the distribution of wetness index among the elements that comprise topography. In addition, the stability analysis was conducted based on the lineament intensity map. Concerning the disaster vulnerability of Mt. Baekdusan region, the south and south west area of Mt. Baekdusan has a highest risk of disaster (grade 4-5) while the risk level decreases in the north eastern region.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.20-27
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• 2012

The flowed soil volume is able to be estimated simply from topographic data of before and after the debris flow. However, it is often difficult to obtain high resolution topographic data before debris flow because debris flow was occurred in mountainous area and airborne Lidar data was mainly surveyed in urban area. For this reason, Woo(2011) developed the topographic restoration method that can reconstruct the topography before the debris flow using airborne Lidar data. In this study, we applied the topographic restoration method on Inje county, Bongwha county and Jecheon city, produced topography data before debris flow that RMSE is from 0.16 to 0.34 m. Also, a soil variation was analyzed by topography data before and after debris flow, and it was used to estimate a real soil volume flowed to downstream and a spatial distribution showing collapses, flows, sedimentations appeared to debris flow.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.67-74
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• 2023

In this study, the risk of landslides was analyzed for planned development sites in mountainous areas. Field survey was conducted on the research area with the slope and valley site. The criteria for evaluating the risk of landslides in the field survey were based on the risk assessment table of the Korea Forest Service Notice No. 2023-10. The research area has 13 slopes and 11 valleys. As a result of evaluating the risk area, two slopes and two valley were found to be dangerous sites in each. Numerical simulation was performed on the investigated risk areas to predict the spread of damage. The debris flow was simulated to have an affect on roads and buildings located in the lower part of the basin, and it was determined that a disaster prevention facility was nacessary to minimize damage. This information can be used to determine the impact of disasters before carrying out mountain development.

• Journal of Forest and Environmental Science
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• v.6 no.1
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• pp.8-17
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• 1989

Recent development of industry and urbanization in the interior of mountainous area increases the possibility of occurence of natural disaster, such as flood, landslide and deblis-flow. Erosion control facilities, which were the most significant activity to riverbed fixiation, were constructed at the downstream of the experimental basin. In the mountain torrents, the complex bed load transport has occurred by the drift of running water, and resulting in a formation of terrace deposits. Especially, channel migration caused by scouring and deposition frequently occurs at the wide areas of the river bed. Consequently, the unsymmetrical river bed charactristics indicate the degree of the channel migration.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.272-278
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• 2009

This study was conducted to develope calculation method of standard rainfall, which was used for predicting the outbreaking time of disaster by using Tank model, on warning and evacuation for soil sediment disaster. We investigate adeption possibility of developed method through comparing storage function method with Tank model. We calculated storage amount rainfall by storage function method and Tank model with 36 hillslope failures which have record on outbreaking time of disaster. The result in case of Sedimentary (quarternary period) showed that the difference of outbreaking time was 1.6 hour in case of tank model, but 3.2 hour in case of storage function method. In addition, the deviation of the peak storage were 7% in case of tank model, but 63% in case of storage function method. Total evacuation period was analyzed by using observed 5 years (1993-1997) rainfall data as well as each standard rainfalls which were determinated by two methods. The result showed that evacuation time by storage function method was about twice as many as that by tank model. Therefore, we concluded that calculation by tank model for predicting the outbreaking time of disaster was more useful and accurate than storage function method.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.13-23
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• 2021

This study intended to assess the reliability of topographic data using satellite imaging data. The topographical data using actual instrumentation data and satellite image data were established and applied to the rainfall-leak model, S-RAT, and the topographical data and outflow data were compared and analyzed. The actual measurement data were collected from the Water Resources Management Information System (WAMIS), and satellite image data were collected from MODIS observation sensors mounted on Terra satellites. The areas subject to analysis were selected for two rivers with more than 80% mountainous areas in the Han River basin and one river basin with more than 7% urban areas. According to the analysis, the difference between instrumentation data and satellite image data was up to 50% for peak floods and up to 17% for flood totals in rivers with high mountains, but up to 13% for peak floods and up to 4% for flood totals. The biggest difference in the video data is Landuse, which shows that MODIS satellite images tend to be recognized as cities up to 60% or more in urban streams compared to WAMIS instrumentation data, but MODIS satellite images are found to be less than 5% error in forest areas.

• Atmosphere
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• v.31 no.1
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.408-417
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• 2016

The frequent regional torrential or heavy rain and typhoon mostly caused by climate change has resulted in sediment disasters particularly in mountainous or hilly areas. More than 65% of South Korea is mountainous and development and rapid urbanization has brought lots of steep sloping industrial complexes, which are adjacent to cities. Such continuous urbanization and industrialization can result in an increase in serious damage to those places. Korea has very high population density so sediment disaster could result in a tremendous loss of property and life. A recent 10-year (2001~2010) study of the average annual loss shows 68 casualties and property loss of 1.7044 trillion Won(?), which indicates a 20% and 25% decrease for both life and property, respectively, but urban areas are experiencing increasing damage. In this paper, a comprehensive simulator composed by references, analyses, and the recent technologies was applied to visualize the scale of the damaged Woomyeon-san (Mt.) and verify the performance of the simulator.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.119-132
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• 2012

Recently, forest soil sediment disasters resulting from locally concentrated heavy rainfall have been occurring frequently in steep slope areas. The importance of landslide hazard map is emerging to analyze landslide vulnerable areas. This study was carried out to develop HyGIS-Landslide based on Hydro Geographic Information System in order to analyze forest soil sediment disaster in the mountainous river basin. HyGIS-Landslide is one of HyGIS components designed by considering the landslide hazard criteria of Korea Forest Service. It could show the distribution of landslide hazard areas after calculating the spatial data. In this system, the user could reset the weight of hazard criteria to reflect the regional characteristics of the landslide area. This component provided user interface that could make the latest spatial data available in the area of interest. HyGIS-Landslide could be applied to the surveyor's compensation score and it was possible to reflect the landslide risk exactly through it. Also, it could be used in topographic analysis techniques providing spatial analysis and making topographical parameters in HyGIS. Finally the accuracy could be acquired by calculating the landslide hazard grade map and landslide mapping data. This study applied HyGIS-Landslide at the Gangwon-do province sample site. As a result, HyGIS-Landslide could be applied to a decision support system searching for mountainous disaster risk region; it could be classified more effectively by re-weighting the landslide hazard criteria.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.