• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.243-255
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• 2003

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and ${\gamma}$ operator shows the better prediction power than the results using max and min operators from the validation procedure.

• Journal of the Korean Geotechnical Society
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• v.33 no.12
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• pp.7-20
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• 2017

In this study, effects of grain size distribution on the shear strength and rheological properties are investigated for coarse- and fine-grained soils by using direct shear apparatus. Shear strengths are estimated for fine-grained soils with the maximum particle size of 0.075 mm and coarse-grained soils with the maximum particle size of 0.425 mm and fine contents of 17% prepared at dry and liquid limit states. The direct shear tests are conducted under the relatively slow shear velocity, which corresponds to the reactivated landslide or debris flow after collapse according to the landslide classification. In addition, for the evaluation of rheological properties, residual shear strengths for both fine- and coarsegrained soils prepared under liquid limit states are obtained by multiple reversal shear tests under three shear velocities. From the relationship between residual shear strengths and shear rates, Bingham plastic viscosity and yield stress are estimated. The direct shear tests show that cohesions of fine-grained soil are greater than those of coarse-grained soil at both dry and liquid limit states. However, internal friction angles of fine-grained soil are smaller than those of coarse-grained soil. In case of rheological parameters, the plastic viscosity and yield stress of fine-grained soils are greater than those of coarse-grained soils. This study may be effectively used for the prediction of the reactivated landslide or debris flow after collapse.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.430-439
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• 2019

In this study, a nonlinear impact analysis was performed to evaluate the safety and damage of an eco-pillar debris barrier with a hollow cross-section, which was proposed to improve constructability and economic efficiency. The construction of concrete eco-pillar debris barriers has increased recently. However, there are no design standards concerning debris barriers in Korea, and it is difficult to find a study on performance evaluations in extreme environments. Thus, an analysis of an eco-pillar debris barrier was done using the rock impact speed, which was estimated from the debris flow velocity. The diameters of rocks were determined by ETAG 27. The impact position, angles, and rock diameter were considered as variables. A concrete nonlinear material model was applied, and the estimation of damage was done by ABAQUS software. As a result, the damage ratio was found to be less than 1.0 at rock diameters of 0.3 m and 0.5 m, but it was 1.39 when the diameter was 0.7 m. This study could be used as basic data on impact force in the design of the cross section of an eco-pillar debris barrier.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.63-63
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• 2015

산사태나 토석류와 같은 산지재해가 빈발하고 인명과 재산의 피해가 증가함에 따라 적절한 대책이 시급하게 요구되고 있다. 토석류 매커니즘에 대한 많은 선행 연구가 이루어졌으며, 토석류 수치해석은 설정된 지형이나 수문곡선 등의 조건하에서 토석류의 발생, 이동, 퇴적, 범람범위 등의 거동을 표현하는 것이다. 본 연구에서는 토석류 해석에 자주 이용되는 Takahashi model과 Egashira model을 이용하여 침식과 퇴적과정을 모의하였다. Takahashi model에서는 토석류, 소류 상집합유동 등에 따라 유동형태가 달라지는 반면, Egashira model에서는 유동형태의 구분없이 침식이나 퇴적이 일어나지 않는 평형경사를 상정하여 하상구배가 평형구배를 만족시키는 방향으로 침식/퇴적이 발생한다고 평가한다. 이러한 각각의 토석류 모델을 이용하여 여러 가지 하상에서의 침식현상, 퇴적현상을 수치모의하고 각 모델의 해석결과를 비교하였다. 또한 이동상에서의 침식/퇴적거동을 해석하였으며, 실제 지형에 대한 적용가능성을 검토하였다. 이러한 토석류 수치해석 결과를 이용하여 토석류재해의 규모나 범위를 예측할 수 있으며 실제로 발생한 토석류재해의 검증, 사방구조물의 기능평가에도 유용하게 이용될 수 있다. 본 연구에서는 1차원해석에 머물러 있으나 계류상의 거동을 표현하고 사방구조물의 위치선정을 위해서는 무리가 없으며, 향후 2차원해석을 통해 범람거동을 해석한다면 토석류위험지도 작성에 유효할 것으로 판단되었다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.1
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• pp.29-35
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• 1993

The vegetation such as grass, shrub, tree has been used to control the erosion and stabilize the slope for a long time. But the effects of vegetation on slope area is usually neglected in traditional stability analyses. There are many errors in slope analyses in thin soil mantles. Therefore the effects of vegetation is an important factor. But it is difficult and complex to represent the vegetation influence quantitatively in stability analysis. In this study, authors choose the landslide region at the Kum sung dong Kum-jung ku Pusan as a model area. Authors analyzed the degree of slope with the aerial photo interpretation and DTM data extracted from the topographic map, and the relationship of D.B.H. (diameter of breast height), height, and age of tree in field investigation data. Finally authors know the fact that landslide take place approximately 10 or 20 years later in arbitrary afforestable area where the degree of slope is 27. The prevention effect must be considered in the control of vegetation.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.185-190
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• 2005

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. In order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The six landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The six factors consist of two topographic factors and four geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 86.5% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.165-172
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• 2015

The extreme weather conditions become frequent and severe with global warming. To prevent and cope forest disaster like a forest fire, we need an accurate micrometeorological prediction system for mountainous regions. This study addressed the forest fires occurred at Bonghwa and Gangneung in March, 2013. We constructed and optimized the prediction system that were required to interpret and simulate the forest micrometeorology. At first, we examined WRF physical sensitivity. Subsequently, KMA AWS observation data were assimilated using three-dimensional variation data assimilation method. The effectiveness of the assimilation was examined by using AWS observations enhanced with the Forest Research Institute observations. Finally, The 100 meters spatial resolution wind data were obtained by using the MUKLIMO for the given wind vector from WRF.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.533-538
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• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.208-220
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• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.