• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.533-538
• /
• 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
• /
• v.18 no.4
• /
• pp.208-220
• /
• 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.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.3
• /
• pp.48-62
• /
• 2018

In this study, landslide of debris flow occurred at 51 sites around Daeryounsan located in between Chuncheon-si and Hongcheon-gun during July in 2013 were investigated in field and behavior characteristics of debris flow were analyzed on the basis of records of rainfall and site investigation. According to debris flow types of channelized and hill slope, location and slope angle of initiation and deposit zone, and width and depth of erosion were investigated along entire runout of debris flow. DEM(Digital Elevation Model) of Daeryounsan was constructed with digital map of 1:5,000 scale. Land slide hazard was estimated using SINMAP(Stability INdex MAPping) and the predicted results were compared with field sites where debris flow occurred. As analyzed results, for hill slope type of debris flow, predicted sites were quite comparable to actual sites. On the other hand, for channelized type of debris flow, debris flow occurrence sites were predicted by using stability index associated with topographic wetness index. As analyzed results of 4 different conditions with the parameter T/R, Hydraulic transmissivity/Effective recharge rate, proposed by NRCS (Natual Resources Conservation Service), predicted results showed more or less different actual sites and the degree of hazard tended to increase with decrease of T/R value.

• Journal of the Korean GEO-environmental Society
• /
• v.19 no.9
• /
• pp.29-36
• /
• 2018

Several studies have been conducted on monitoring system and automatic measuring instruments to prevent slope failure in advance in Korea and overseas. However, these studies have quite complex structure. Since most of the measurement systems are installed on the slope surface, the researches are carried on the measurement system that detects sign of slope collapse in advance and alerts are still unsatisfactory. In this study, slope collapse experiments were carried out to understand the slope failure mechanism according to rainfall conditions. The water content and displacement behavior at the early stage of the slope failure were analyzed through the measurement of the ground displacement and water content. The results of this study can be used by local government as a basic data for the design of slope failure alarm system to evacuate residents in case of slope failure or landslide due to heavy rainfall.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.471-471
• /
• 2017

최근 기상 이변으로 인해 홍수, 가뭄 등과 같은 수재해가 빈번히 발생되고 있다. 이에 수재해예방 등의 안전 기술과 관련된 관심이 증가되고 있다. 수재해 예방을 위해서는 먼저 홍수, 가뭄 등과 같은 수재해 감시가 필요하며, 이를 위해 위성, 레이더 등으로 관측된 자료를 활용한 수문인자 정보는 매우 중요하다. 미국 NASA의 LIS(Land Information System)는 위성 및 지상관측 자료를 활용하여 홍수, 가뭄, 기상, 산사태, 농업 등의 정보를 생산할 수 있는 프레임워크이다. LIS는 크게 지표면 모델 및 자료동화를 위한 변수들의 전처리 과정(LDT), 지표면 모델을 활용한 분석 및 자료동화 과정(LDAS) 및 분석된 자료의 검보정(LVT) 과정으로 구성되어 있다. LIS에서 산출 가능한 인자는 Energy Balance, Water Balance, Surface/Subsurface State, Evaporation, Hydrologic, Cold Season Processes, Compared Data, Carbon 등 9개로 분류되며 약 78개의 인자를 산출한다. 홍수, 가뭄 등과 같은 수재해 감시를 위한 수문인자는 강수량, 증발산량, 토양수분, 지표면 온도 등을 비롯한 여러 가지 인자들이 필요하다. LIS는 주로 미국, 캐나다 등 평활한 지역에 활용되어 공간해상도는 약 10km(0.1deg) 이하로 자료를 산출한다. 산악 지형이 대부분인 한국 지형에 적용하기에는 자료의 정확성이 낮아 10km 이상의 공간해상도 자료가 필요하며, 한국형 수재해정보 플랫폼에서 홍수, 가뭄 등의 기초자료로 사용하기 위한 수문인자의 선정이 필요하다. 이에 본 연구에서 NASA LIS를 통해 산출 가능한 인자를 정리하고 한국형 수재해 정보플랫폼에 활용 가능한 수문인자의 항목을 조사하였다. 홍수, 가뭄 등 수재해 분석에 필요한 기초자료는 강우량, 유출량, 잠재적 증발산량, 식생의 증산량, 토양수분, 표면온도, 알베도 등의 수문인자이며, NASA LIS에서 이와 같은 수문인자 산출이 가능하다. NASA와 국제공동 연구중인 한국형 물순환분석 프레임워크(K-LIS(안)) 개발을 통해 한국 지형에 적합한 홍수 및 가뭄 등의 수재해 감시 평가 예측이 가능할 것이며, K-LIS에서 산출되는 고해상도의 수문인자들을 수재해 정보 웹 포털의 정보 제공 서비스를 통하여 손쉽게 접근 가능할 것으로 사료된다.

• Journal of Korean Society of Disaster and Security
• /
• v.12 no.2
• /
• pp.65-72
• /
• 2019

More than 64% of Korea's land is occupied by mountain regions, which have terrain characteristics that make it vulnerable to mountain disasters. The trails of Taebaeksan Mountain National Park-the region considered in this study-are located in the vicinity of steep slopes, and therefore, the region is vulnerable to landslides and debris flow during heavy storms. In this study, a slope stability model, which is a deterministic analysis method, was used to examine the potential occurrence of landslides. According to the soil classification of the detailed soil map, the specific weight of soil, effective cohesion, internal friction angle of soil, effective soil depth, and ground slope were used as the parameters of the model, and slope stability was evaluated based on the DEM of a 1 m grid. The results of the slope stability analysis showed that the more hazardous the area was, the closer the ratio of groundwater/effective soil depth is to 1.0. Further, many of the private houses and commercial facilities in the lower part of the national park were shown to be exposed to danger.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.3
• /
• pp.59-66
• /
• 2016

The 2011 debris flow in Mt. Umyeonsan in Seoul, South Korea caused significant damages to the surrounding urban area, unlike other similar incidents reported to have occurred in the past in the country's mountainous regions. Accordingly, landslides and debris flows cause damage in various surroundings, regardless of mountainous area and urban area, at a great speed and with enormous impact. Hence, many researchers attempted to forecast the extent of impact of debris flows to help minimize the damage. The most fundamental part in forecasting the impact extent of debris flow is to understand the debris flow behavior and sedimentation mechanism in complex three-dimensional topography. To understand sedimentation mechanism, in particular, it is necessary to calculate the amount of energy and erosion according to debris flow behavior. The previously developed debris flow models, however, are limited in their ability to calculate the erosion amount of debris flow. This study calculated the extent of damage caused by a massive debris flow that occurred in 2011 in Seoul's urban area adjacent to Mt. Umyeonsan by using DEM, created from aerial photography and airborne LiDAR data, for both before and after the damage; and developed and compared a debris flow behavioral analysis model that can assess the amount of erosion based on energy theory. In addition, simulations using the existing debris flow model (RWM, Debris 2D) and a comprehensive comparison of debris flow-stricken areas were performed in the same study area.

• Journal of Korean Society of Forest Science
• /
• v.83 no.2
• /
• pp.175-190
• /
• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Journal of Environmental Impact Assessment
• /
• v.15 no.6
• /
• pp.373-383
• /
• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

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