• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.302-311
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• 2008

There have been repetitive landslides and debris flows on natural terrain induced by intensive rainfalls which have never been experienced during the last a few decades in Korea. Frequencies and magnitudes of landslides and debris flows are steeply increased after 2000 resulting in huge damages of human beings and facilities. According to a statistical data from NEMA, the human deaths induced by landslides and slope hazards occupies 22.3% of the total human deaths by all the natural hazards in Korea during the last 30 years. Among the human deaths by landslides and slope hazards, 85% of the damages were caused by landslides and debris flows on natural hazards. Therefore, this paper summarizes important events of landslides and debris flows, their characteristics, and suggests some methods of damage mitigation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1252-1254
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• 2003

Debris flows associated with landslides occur as one of the most devastating natural disasters that threat Taiwan. Typically, three essential factors are needed simultaneously to trigger debris flow, namely sufficient soils and rocks, favorable slope, and abundant water. Among the three essentials, the slope is natural and static without external forcing, while the landslide is generally induced by earthquake or rainfall events, and the water is produced by heavy rainfall events. In this study, we analyzed the landslides triggered by the typhoons Herb (1996) and typhoon Troaji (2001). It is concluded that the statistical data are useful to quantify the threshold of the potential landslide area. Then, the possibility to prevent the debris flow occurrence may be increased.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.33-43
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• 2023

The impact of prolonged rainfall, such as during the monsoon season or intense concentrated rainfall over a short period, can lead to mountainous disasters such as landslides and debris flows. These events, such as landslides and debris flows, cause both human and material damage, prompting the implementation of various measures and research to prevent them. In the context of researching debris flow disasters, numerical models for debris flows provide a relatively simple way to analyze the risk in a study area. However, since empirical equations are applied in these models, yielding different results and variations in input variables across models, the validation of numerical models becomes essential. In this study, a numerical model for debris flows was employed to compare and analyze the mitigation effects of facilities such as check dams and water channel work, aiming to reduce the damage caused by debris flows.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.248-248
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• 2023

In recent years, the number of landslides in Korea has been increasing due to extreme weather events such as localized heavy rainfall and typhoons. Landslides often occur with debris flows, land subsidence, and earthquakes. They cause significant damage to life and property. 64% of Korea's land area is made up of mountains, the government wanted to predict landslides to reduce damage. In response, the Korea Forest Service has established a 'Landslide Information System' to predict the likelihood of landslides. This system selects a total of 13 landslide factors based on past landslide events. Using the LR technique (Logistic Regression) to predict the possibility of a landslide occurrence and the accuracy is known to be 0.75. However, most of the data used for learning in the current system is on landslides that occurred from 2005 to 2011, and it does not reflect recent typhoons or heavy rain. Therefore, in this study, we will apply a total of six machine learning techniques (KNN, LR, SVM, XGB, RF, GNB) to predict the occurrence of landslides based on the data of Inje, Gangwon-do, which was recently produced by the National Institute of Forest. To predict the occurrence of landslides, it is necessary to process converting landslide events and factors data into a suitable form for machine learning techniques through ArcGIS and Python. In addition, there is a large difference in the number of data between areas where landslides occurred or not. Therefore, the prediction was performed after correcting the unbalanced data using Tomek Links and Near Miss techniques. Moreover, to control unbalanced data, a model that reflects soil properties will use to remove absolute safe areas.

• Journal of the Korean GEO-environmental Society
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• v.15 no.6
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• pp.67-73
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• 2014

Landslide disasters including debris flows are the one of the most frequent natural disasters in Korea, and losses of lives and property damages due to these catastrophic events have been increased every year. Various mitigation programs and related policies have been conducted in order to respond and prepare landslide disasters. Most landslide reduction programs are, however, focused on recovery actions after the disasters and lead to unrealistic consequences to the affected people and their properties. The main objective of this study, therefore, is to evaluate the landslide hazard based on the identification of geomorphological features, which is for the preparedness of the landslide disasters. Two methodologies, SINMAP and vector dispersion analyses are used to simulate those characteristics where landslides are actually located. Results showed that both methods well discriminate geomorphic features between stable and unstable domains. This proves that geomorphological characteristics well describe a relationship with the existing landslide hazard. SINMAP analysis which is based on the consecutive model considering external factors like infiltration is well identify the landslide hazard especially for debris flow type landslides rather than vector dispersion focusing on a specific area. Combining with other methods focusing specific characteristics of geomorphological feature, accurate landslide hazard assessments are implemented.

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

• Journal of the Korean Geotechnical Society
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• v.30 no.6
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• pp.51-59
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• 2014

Shallow landslides and debris flows are a common form of soil slope instability in South Korea. These events may be generally initiated as a result of intense rainfall or lengthening rainfall duration because of the effects of climate change. This paper presents the evaluation of rainfall-induced natural soil slope stability and reinforced soil slope instability under vertical load (railway or highway load) throughout South Korea based on quantitative analysis obtained from 58 sites rainfall observatories for 38 years. The slope stability was performed for infinite and geogrid-reinforced soil slopes by taking an average of maximum rainfall every ten years from 1973 to 2010. Seepage analysis is carried out on unsaturated soil slope using the maximum rainfall at each site, and then the factor of safety was calculated by coupled analysis using saturated and unsaturated strength parameters. The contour map of South Korea shows four stages in 10-year-time for the degree of landslide hazard. The safety factor map based on long term observational data will help prevent rainfall-induced soil slope instability for appropriate design of geotechnical structures regarding disaster protection.