• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.507-516
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• 2015

Debris flow is a representative natural disaster in Korea and occurs frequently every year. Recently, it has caused considerable damage to property and considerable loss of life in both mountainous and urban regions. Therefore, It is necessary to estimate the scope of damage for a large area in order to predict the debris flow. A response model such as the random walk model(RWM) can be used as a useful tool instead of a physics-based numerical model. RWM is a probability model that simplifies both debris flows and sedimentation characteristics as a factor of slopes for a subjective site and represents a relatively simple calculation method compared to other debris flow behavior calculation models. Although RWM can be used to analyzing and predicting the scope of damage caused by a debris flow, input variables for terrain conditions are yet to be determined. In this study, optimal input variables were estimated using DEM generated from the Aerial Photograph and LiDAR data of Mt. Umyeon, Seoul, where a large-scale debris flow occurred in 2011. Further, the deposition volume resulting from the debris flow was predicted using the input variables for a specific area in which the deposition volume could not be calculated because of work restoration and the passage of time even though a debris flow occurred there. The accuracy of the model was verified by comparing the result of predicting the deposition volume in the debris flow with the result obtained from a debris flow behavior analysis model, Debris 2D.

• Journal of the Korean Society of Safety
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• v.33 no.1
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• pp.128-134
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• 2018

With the increase in frequency of typhoons and heavy rains following the climate change, the scale of damage from the calamities in the mountainous areas has been growing larger and larger, which is different from the past. For the case of Korea where 64% of land is consisted of the mountainous areas, establishment of the check dams has been drastically increased after 2000 in order to reduce the damages from the debris flow. However, due to the lack of data on scale, location and kind of check dams established for reducing the damages in debris flow, the measures to prevent damages based on experience and subjective basis have to be relied on. Under this study, the high-precision DEM data was structured by using the terrestrial LiDAR in the Jecheon area where the debris flow damage occurred in July 2009. And, from the numerical models of the debris flow, Kanako-2D that is available to reflect the erosion and deposition action was applied to install the erosion control facilities (water channel, check dam) and analyzed the effect of reducing the debris flow shown in the downstream.After installing the erosion control facilities, most of debris flow moves along the water channel to reduce the area to expand the debris flow, and after installing the check dam, the flow depth and flux of the debris flow were reduced along with the erosion. However, as a result of analyzing the diffusion area, flow depth, erosion and deposition volume of the debris flow generated from the deposition part after modifying the location of the check dams with the damages occurring on private residences and agricultural land located on the upstream area, the highest reduction effect was shown when the check dam is installed in the maximal discharge points.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.11-18
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• 2012

Debris flow deposition model is a model to predict affected areas by debris flow and random walk model (RWM) was used to build the model. Although the model was proved to be effective in the prediction of affected areas, the model has several free parameters decided experimentally. There are several well-known methods to estimate parameters, however, they cannot be applied directly to the debris flow problem due to the small size of training data. In this paper, a modified neural network, called pseudo sample neural network (PSNN), was proposed to overcome the sample size problem. In the training phase, PSNN uses pseudo samples, which are generated using the existing samples. The pseudo samples smooth the solution space and reduce the probability of falling into a local optimum. As a result, PSNN can estimate parameter more robustly than traditional neural networks do. All of these can be proved through the experiments using artificial and real data sets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.965-974
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• 2013

Multiple debris flows occurred on July 27, 2012 in Mt. Umyeon, which resulted in 16 casualties and severe property demage. Accurate reproducing of the propagation and deposition of debris flow is essential for mitigating these disasters. Through applying FLO-2D model to these debris flows and comparing the results with field observations, we seek to evaluate the performance of the model and to analyse the rheological model parameters. Representative yield stress and dynamic viscosity back-calculated for the debris flows in the northern side of Mt. Umyeon are 1022 Pa and 652 $Pa{\cdot}s$, respectively. Numerical results obtained using these parameters reveal that deposition areas of debris flows in Raemian and Shindong-A regions are well reproduced in 63-85% agreement with the field observations. However, the propagation velocities of the flows are significantly underestimated, which is attributable to the inherent limitations of the model that can't take the entrainment of bed material and surface water into account. The debris flow deposition computed in Hyeongchon region where the entrainment is not significant appears to be in very good agreement with the field observation. The sensitivity study of the numerical results on model parameters shows that both sediment volume concentration and roughness coefficient significantly affect the flow thickness and velocity, which underscores the importance of careful selection of these model parameters in FLO-2D modeling.

• Journal of the Korean GEO-environmental Society
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• v.11 no.3
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• pp.63-68
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• 2010

The purpose of this paper is estimating of the amount of debris flow in hazard area using terrestrial LiDAR surveying data. Jecheon area was selected for this study. Then, the surveyed LiDAR information of DEM and 1:5000 digital map of DEM have been compared with each other and the amount of debris flow has been estimated. The result of this study was shown that the amount of erosion was $24,150m^3$ and deposition was $14,296m^3$. Well shape of channelized debris flow, hillslope debris and deposition at the bending reach of a channel can be found in the area. This study on estimation of the amount of debris flow was expected to provide more informations for debris flow of disaster mitigation and simulation work.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.54-63
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• 2010

The intensive rainfall over 455 mm occurred between on 9 to 14 July 2009 triggered debris flows around the mountain area in Jecheon County. We mapped the debris flow area and estimated the debris flow volume using a high resolution digital elevation model (DEM) generated respectively from terrestrial LiDAR (Light Detection And Ranging) and topographic maps. For the LiDAR measurement, the terrestrial laser scanning system RIEGL LMS-Z390i which is equipped with GPS system and high-resolution digital camera were used. After the clipping and filtering, the point data generated by LiDAR scanning were overlapped with digital map and produced DEM after debris flow. The comparison between digital map and LiDAR scanning result showed the erosion and deposition volumes of about $17,586m^3$ and $7,520m^3$, respectively. The LiDAR data allowed comprehensive investigation of the morphological features present along the sliding surface and in the deposit areas.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.33-34
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• 2012

토석류 퇴적 모델은 토석류에 의한 피해지 예측을 위해 그 효용성이 입증된 모델이지만 이를 이용하기 위해서는 몇 가지 파라미터를 필요로 한다. 파라미터를 자동으로 추정하기 위한 방법은 여러 가지가 있지만 토석류에 의한 피해지 예측을 위한 데이터는 충분히 양을 확보하기가 어려우므로 기존의 학습 기법을 적용하는데 어려움이 있다. 본 논문에서는 인공 신경망을 학습시키는 과정에서 기존 샘플로부터 의사 샘플을 생성하고 이를 학습에 사용함으로써 보다 안정적인 학습이 가능한 의사 샘플 신경망을 제안하였다. 제안한 의사 샘플 신경망은 해공간을 평탄화시킴으로써 잘못된 국부 최적해에 빠질 확률을 줄여주고 따라서 보다 안정적인 파라미터 추정이 가능하다는 사실을 실험을 통해 확인할 수 있다.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.71-78
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• 2022

Recently, mountain disasters such as landslides and debris flows have flowed along mountain streams and hit residential areas and roads, increasing damage. In this study, in order to reduce damage and analyze causes of mountain disasters, field surveys and Terrestrial LiDAR terrain analysis were conducted targeting debris flow areas, and debris flow flow processes were simulated using FLO-2D and RAMM models, which are numerical models of debris flows. In addition, the debris flow deposition area was calculated and compared and analyzed with the actual occurrence section. The sedimentation area of the debris flow generation section of the LiDAR scan data was estimated to be approximately 21,336 ㎡, and was analyzed to be 20,425 ㎡ in the FLO-2D simulation and 19,275 ㎡ in the case of the RAMMS model. The constructed topographical data can be used as basic data to secure the safety of disaster risk areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.467-474
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• 2011

Debris flow is one of the most hazardous natural processes in mountainous regions. The degradation of discharge capacity of drainage facilities due to debris flows may result in damages of properties and casualty as well as road. Understanding and accurate reproducing flow behaviour of debris flows at various conditions, such as sediment volume concentration and approaching channel and culvert slopes, are prerequisite to develop advanced design criteria for drainage facilities to prevent such damages. We carried out a series of laboratory experiments of debris flows in a rectangular channel of constant width with an abrupt change of bottom slope. The experimental flume consists of an approaching channel part with the bed slope ranging $15^{\circ}$ to $30^{\circ}$ and the test channel with slope ranging from $0^{\circ}$ to $12^{\circ}$ which mimics a typical drainage culvert. The experiments have been conducted for 22 test cases with various flow conditions of channel slopes and sediment volume concentration of debris flows to investigate those effects on the behaviour of debris flows. The results show that, according to sediment volume concentration, the depth of debris flow is approximately 50% to 150% larger than that of fresh water flow at the same flow rate. Experimental results quantitatively present that flow behaviour and deposit history of debris flows in the culvert depend on the slopes of the approaching and drainage channels and sediment volume concentration. Based on the experimental results, furthermore, a logistic model is developed to find the optimized culvert slope which prevents the debris flow from depositing in the culvert.

• Journal of the Korean Geotechnical Society
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• v.27 no.6
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• pp.49-61
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• 2011

This paper presents the applicability of rheological models for describing fine-laden debris flows and analyzes the flow characteristics as a function of grain size. Two types of soil samples were used: (1) clayey soils - Mediterranean Sea clays and (2) silty soils - iron ore tailings from Newfoundland, Canada. Clayey soil samples show a typical shear thinning behavior but silty soil samples exhibit the transition from shear thinning to the Bingham fluid as shear rate is increased. It may be due to the fact that the determination of yield stress and plastic viscosity is strongly dependent upon interstructrual interaction and strength evolution between soil particles. So grain size effect produces different flow curves. For modeling debris flows that are mainly composed of fine-grained sediments (<0.075 mm), we need the yield stress and plastic viscosity to mimic the flow patterns like shape of deposition, thickness, length of debris flow, and so on. These values correlate with the liquidity index. Thus one can estimate the debris flow mobility if one can measure the physical properties.