• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.28-35
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• 2012

Rainfall is widely recognized as a major landslide-triggering factor. Most of the latest landslides that occurred in South Korea were caused by short-duration heavy rainfall. However, the relationship between rainfall characteristics and landslide occurrence is poorly understood. To examine the effect of rainfall on landslide occurrence, cumulative rainfall(mm) and rainfall intensity(mm/hr) of serial rain and antecedent rainfall(mm) were analyzed for 18 landslide events that occurred in the southern and central regions of South Korea in June and July 2011. It was found that all of these landslides occurred by heavy rainfall for one or three days, with the rainfall intensity exceeding 30 mm/hr or with a cumulative rainfall of 200 mm. These plotted data are beyond the landslide warning criteria of Korea Forest Service and the critical line of landslide occurrence for Gyeongnam Province. It was also found that the time to landslide occurrence after rainfall start(T) was shortened with the increasing average rainfall intensity(ARI), showing an exponential-decay curve, and this relation can be expressed as "T = $94.569{\cdot}exp$($-0.068{\cdot}ARI$)($R^2$=0.64, p<0.001)". The findings in this study may provide important evidences for the landslide forecasting guidance service of Korea Forest Service as well as essential data for the establishment of non-structural measures such as a warning and evacuation system in the face of sediment disasters.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• Journal of the Korean GEO-environmental Society
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• v.24 no.4
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• pp.5-11
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• 2023

Impulse waves generated by landslides near water bodies can lead to fatal damage to human life and surrounding infrastructure. These impulse waves are generally called landslide-impulsed waves and occur without being limited to a specific area. Recently, localized torrential rains have frequently occurred due to the influence of abnormal weather, both the frequency and scale of landslides occurring in Korea are increasing. Therefore, in this study, the experiments were conducted according to the mass ratio of the landslide models, and among the characteristics of the generated landslide-impulse waves. And the wave amplitude was observed and analyzed. In this study, a total of 75 experiments were conducted by repeating the experiment 5 times for 15 cases with mass ratios of 5 landslide models and 3 types of slope angles. As a result of experiments with different mass ratios of landslide models, if the landslides have the same initial energy, the size of the landslide-impulse waves generated by mixing granular and block forms is higher than the size of the landslide-impulse waves generated by pure granular and block landslides. It is analyzed that the size may be larger.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.376-386
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• 2023

Due to climate changes, landslide hazards in the Republic of Korea (hereafter South Korea) continuously increase. To establish the effective landslide mitigation strategies, such as erosion control works, landslide hazard estimation in the long-term perspective should be proceeded considering the influence of climate changes. In this study, we examined the change in landslide-damaged areas in South Korea responding to climate change scenarios using the multivariate regression method. Data on landslide-damaged areas and rainfall from 1981-2010 were used as a training dataset. Sev en indices were deriv ed from rainfall data as the model's input data, corresponding to rainfall indices provided from two SSP scenarios for South Korea: SSP1-2.6 and SSP5-8.5. Prior to the multivariate regression analysis, we conducted the VIF test and the dimension analysis of regression model using PCA. Based on the result of PCA, we developed a regression model for landslide damaged area estimation with two principal components, which cov ered about 93% of total v ariance. With climate change scenarios, we simulated landslide-damaged areas in 2030-2100 using the regression model. As a result, the landslide-damaged area will be enlarged more than the double of current annual mean landslide damaged area of 1981-2010; It infers that landslide mitigation strategies should be reinforced considering the future climate condition.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Korea Petroleum Association Journal
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• no.8 s.126
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• pp.96-99
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• 1991

• 산림경영
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• s.151
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• pp.51-51
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• 2001

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.1-10
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• 2009

Recently the large scale of forest disaster such as landslide and forest fire gives a very bad impact on not only forest ecosystem but also farm business so that it has became the main issue of environmental problems. In this study, the landslide hazard area forecast method was developed by considering not only the topographic thematic maps based on GIS and satellite images but also amount of rainfall data, which are very important factors of landslide. Uljin-gun was selected as the study area and the GIS weight score and overlay analysis were applied to topographical map and meteorological observation map. Finally the landslide area distribution map was constructed by considering the evaluation criteria. Also, the accuracy could be acquired by comparing the landslide hazard area forecast map and real damaged area extracted from satellite image.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.148-156
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• 2015

The purpose of this study was to evaluate the dance attitude differences between gender, majors, and grades. Attitudes-towards-dance inventory(Jadranka et al., 2004) which covered affective, cognitive, and action aspects of attitudes, as well as to reflect, in a similar number of items, both the positive and negative attitude towards dance was used to investigate dance attitude for 483 dancers who are collegian. Male students were more positive attitude in the affective area among dance attitudes than female students(p<.05). Dancers who major practical dances revealed more positive attitude in the affective area than dancers majoring ballet and Korean treditional dance(p<.05). In addition, freshmen have a more positive dance attitude in the affective area compared with seniors. It was suggested that more subjects should be studied from considering major, local university as future study.