• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.15-24
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• 2016

This study conducted an evaluation of the extent of debris flow damage using SINMAP, which is slope stability analysis software based on the infinite slope stability method, and FLO-2D, a hydraulic debris flow analysis program. Mt. Majeok located in Chuncheon city in the Gangwon province was selected as the study area to compare the study results with an actual 2011 case. The stability of the slope was evaluated using a DEM of $1{\times}1m$ resolution based on the LiDAR survey method, and the initiation points of the debris flow were estimated by analyzing the overlaps with the drainage network, based on watershed analysis. In addition, the study used measured data from the actual case in the simulation instead of existing empirical equations to obtain simulation results with high reliability. The simulation results for the impact of the debris flow showed a 2.2-29.6% difference from the measured data. The results suggest that the extent of damage can be effectively estimated if the parameter setting for the models and the debris flow initiation point estimation are based on measured data. It is expected that the evaluation method of this study can be used in the future as a useful hazard mapping technique among GIS-based risk mapping techniques.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.34-44
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• 1998

The purpose of this study is to provide the basic data for land use in the future, which result from analyzing land use, obtained after studying on the natural environment by Geographic Information System and Remote Sensing. The results of this study are as follows : ·According to the classification of land-cover, agricultural land use is relatively prominent except for overall natural covering. According to the average value of Green Vegetation Index class, the average value of GVI is 3.0, and 45% of the regions have relatively good condition of floral state. ·With a view to natural environment, the survey shows that the altitude of 90% of the total areas is below 400m, and most of them are flattened or moderately-inclined area. Therefore, this region has a good condition to be used for development. · The area for the first class in preservation degree of natural scenery of Namcheon-Myun is 2.3% of the total areas. According to the results about unstable areas on all sides, unstable districs are distributed in so small-scale units that they will be safe from some damages drawn by developing activity. But we have to consider every aspects for the future development of them. In this study, the natural environment-variables are regarded firstly, and effective designation of the land with natural environment is researched too. However, to establish more practical developing plan, ecological and human variables should be regarded.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.105-110
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• 2016

Mountainous disasters such as landslides and debris flow are difficult to forecast. Debris flow in particular often flows along the valley until it reaches the road or residential area, causing casualties and huge damages. In this study, the researchers selected Seoraksan National Park area located at Inje County (Inje-gun), Gangwon Province-where many mountainous disasters occur due to localized torrential downpours-for the damage reduction and cause analysis of the area experiencing frequent mountainous disasters every year. Then, the researchers conducted the field study and constructed geospatial information data by GIS method to analyze the characteristics of the disaster-occurring area. Also, to extract more precise geographic parameters, the researchers scanned debris flow triggering area through terrestrial LiDAR and constructed 3D geographical data. LiDAR geographical data was then compared with the existing numerical map to evaluate its precision and made the comparative analysis with the geographic data before and after the disaster occurrence. In the future, it will be utilized as basic data for risk analysis of mountainous disaster or disaster reduction measures through a fine-grid topographical map.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.