Debris flow in metropolitan area — 2011 Seoul debris flow

Yune, Chan-Young; Chae, Yun-Ki; Paik, Jong-Ho; Kim, Gi Hong; Lee, Seung-Woo; Seo, Heung-Seok

doi:10.1007/s11629-013-2518-7

Cited by 31 publications

(15 citation statements)

References 3 publications

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“…Due to the effects of global climate change, the death rate caused by floods and landslides has been increasing worldwide [2,3], and in the Republic of Korea (hereafter, "Korea"), more than 60 deaths have been occurring annually from meteorological disasters [4]. In 2011, particularly, 16 died within the metropolitan area of Seoul due to localized heavy rain, indicating that there is no safe zone in Korea from meteorological disasters [5]. …”

Section: Introductionmentioning

confidence: 99%

Socio-demographic Characteristics and Leading Causes of Death Among the Casualties of Meteorological Events Compared With All-cause Deaths in Korea, 2000-2011

Lee

Myung

et al. 2013

J Prev Med Public Health

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ObjectivesThis study investigated the socio-demographic characteristics and medical causes of death among meteorological disaster casualties and compared them with deaths from all causes.MethodsBased on the death data provided by the National Statistical Office from 2000 to 2011, the authors analyzed the gender, age, and region of 709 casualties whose external causes were recorded as natural events (X330-X389). Exact matching was applied to compare between deaths from meteorological disasters and all deaths.ResultsThe total number of deaths for last 12 years was 2 728 505. After exact matching, 642 casualties of meteorological disasters were matched to 6815 all-cause deaths, which were defined as general deaths. The mean age of the meteorological disaster casualties was 51.56, which was lower than that of the general deaths by 17.02 (p<0.001). As for the gender ratio, 62.34% of the meteorological event casualties were male. While 54.09% of the matched all-cause deaths occurred at a medical institution, only 7.6% of casualties from meteorological events did. As for occupation, the rate of those working in agriculture, forestry, and fishery jobs was twice as high in the casualties from meteorological disasters as that in the general deaths (p<0.001). Meteorological disaster-related injuries like drowning were more prevalent in the casualties of meteorological events (57.48%). The rate of amputation and crushing injury in deaths from meteorological disasters was three times as high as in the general deaths.ConclusionsThe new information gained on the particular characteristics contributing to casualties from meteorological events will be useful for developing prevention policies.

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Section: Introductionmentioning

confidence: 99%

Socio-demographic Characteristics and Leading Causes of Death Among the Casualties of Meteorological Events Compared With All-cause Deaths in Korea, 2000-2011

Lee

Myung

et al. 2013

J Prev Med Public Health

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“…Specifically, 588 mm of heavy rain, which is about 40% of the annual average rainfall of 1451 mm, was concentrated in Mt. Umyeon for 3 days from July 26 to 28; debris flows caused 16 casualties and dozens of houses were damaged [47]. Debris flows that occurred simultaneously around Mt.…”

Section: Study Areamentioning

confidence: 99%

Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon

et al. 2020

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In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.

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“…In 2011, a total of about 150 landslides occurred around Umyeon mountain after a continuous rainfall, with a maximum recorded amount of 370 mm. Most of the landslides were mobilized into debris-flow, which led to 16 human casualties and damage to 116 buildings surrounding the mountains [48]. One of the regions around Umyeon mountain that was affected by debris-flow is called the Raemian region.…”

Section: Case Study: Umyeon Mountain Seoul Korea In 2011mentioning

confidence: 99%

Hazard Assessment Based on the Combination of DAN3D and Machine Learning Method for Planning Closed-Type Barriers against Debris-Flow

Cheon

Lee

2020

Water

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If a slope located near a densely populated region is susceptible to debris-flow hazards, barriers are used as a mitigation method by placing them in flow channels; i.e., flowpaths. Selecting the location and the design of a barrier requires hazard assessment to determine the width, volume, and impact pressure of debris-flow at the moment of collision. DAN3D (Three-Dimensional Dynamic Analysis), a 3D numerical model for simulating debris-flow, has been widely used to perform hazard assessment; however, solely using DAN3D would be both insufficient and inefficient in finding the optimal barrier location. Therefore, the present study developed a framework that interprets the results from DAN3D simulation without considering any barriers. Then, the framework generates hazard assessment maps showing the impact parameters of debris-flow along the flowpath by various algorithms and machine learning methods, such as the k-means clustering algorithm, and also computes the width of the debris-flow, which is not explicitly calculated in DAN3D. A case study of the debris-flow at Umyeon mountain, Korea, in 2011, was used to generate hazard assessment maps. The maps were demonstrated to be a tool to quickly compute the impact parameters for conceptual barrier design with the aim of finding potential barrier locations.

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Debris flow in metropolitan area — 2011 Seoul debris flow

Cited by 31 publications

References 3 publications

Socio-demographic Characteristics and Leading Causes of Death Among the Casualties of Meteorological Events Compared With All-cause Deaths in Korea, 2000-2011

Socio-demographic Characteristics and Leading Causes of Death Among the Casualties of Meteorological Events Compared With All-cause Deaths in Korea, 2000-2011

Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon

Hazard Assessment Based on the Combination of DAN3D and Machine Learning Method for Planning Closed-Type Barriers against Debris-Flow

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