• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.125-132
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• 2019

In this study, we developed a heavy rainfall risk impact matrix, which can be used to evaluate the influence of heavy rainfall risk, and propose a method to evaluate the impact of heavy rainfall risk. We evaluated the heavy rainfall risk impact for each receptor (Residential, Transport, Utility) on Sadang-dong using the heavy rainfall event on July 27, 2011. For this purpose, the potential risk impact was calculated by combining the impact level and the rainfall depth based on the grid. Heavy Rainfall Risk Impact was calculated by combining with Likelihood to predict the heavy rainfall impact, and the degree of heavy rainfall in the Sadang-dong area was analyzed and presented based on grid.

• Journal of Korea Water Resources Association
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• v.52 no.3
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• pp.173-185
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• 2019

The Korean Peninsula is considered as one of the most typhoon related disaster prone areas. In particular, the potential risk of flooding in coastal areas would be greater when storm surge and heavy rainfall occurred at the same time. In this context, understanding the mechanism of the interactions between them and estimating the risk associated with the concurrent occurrence are of particular interests especially in low-lying coastal areas. In this study, we developed a Poisson-Generalized Pareto (Poisson-GP) distribution based storm surge frequency analysis model to combine the occurrence of the exceedance of a threshold, that is the peaks over threshold (POT), within a Bayesian framework. The storm surge frequency analysis technique developed through this study might contribute to the improvement of disaster prevention technology related to storm surge in the coastal area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.211-218
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• 2019

The Ministry of Land, Infrastructure and Transport is planning to launch CAS-500 (Compact Advanced Satellite 500) 1 and 2 in 2019 and 2020. Satellite image information collected through CAS-500 can be used in various fields such as global environmental monitoring, topographic map production, analysis for disaster prevention. In order to utilize in various fields like this, it is important to get the location accuracy of the satellite image. In order to establish the precise geometry of the satellite image, it is necessary to establish a precise sensor model using the GCP (Ground Control Point). In order to utilize various fields, step - by - step automation for orthoimage construction is required. To do this, a database of satellite image GCP chip should be structured systematically. Therefore, in this study, we will analyze various techniques for automatic GCP extraction for precise geometry of satellite images.

• Journal of the Korean GEO-environmental Society
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• v.20 no.4
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• pp.23-29
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• 2019

Heavy rainfall is in causing debris flow by recent climate change and causes much damage in the downstream. The debris flow from the mountainous area runs to the downstream, repeating sedimentation and erosion, and appears as a fluidized soil-water mixture. Continuity equation and momentum equation were applied to analyze the debris flow with strong mobility, and the sedimentation and erosion velocity with fine particle fractions were also applied. This study is to analyze the behavior of debris flow at the downstream end for the variation of the amount of sediments can occur in the upstream of the mountain. Analysis of sediment volume concentration at the downstream end of the channel due to the variance of the length of pavement of the granulated soils resulted in the higher the supply flow discharge and the longer the length of pavement, the greater the difference in the level of sediment concentration and the earlier the point of occurrence of the inflection point. The results of this study will provide good information for determining the erosion-sedimentation velocity rate which can detect erosion and sedimentation on steep slopes.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.31-39
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• 2021

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.617-628
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• 2021

Drought is a major natural disaster that causes serious social and economic losses. Local drought forecasts can provide important information for drought preparedness. In this study, we propose a new machine learning model that predicts drought by using historical drought indices and meteorological data from 10 sites from 1981 to 2020 in the southeastern part of the Korean Peninsula, Busan-Ulsan-Gyeongnam. Using Bayesian optimization techniques, a hyper-parameter-tuned Random Forest, XGBoost, and Light GBM model were constructed to predict the evaporative demand drought index on a 6-month time scale after 1-month. The model performance was compared by constructing a single site model and a regional model, respectively. In addition, the possibility of improving the model performance was examined by constructing a fine-tuned model using data from a individual site based on the regional model.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.188-198
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• 2022

Some deep faults do not reach the ground surface and are seldom recognized. Gokgang Fault area in the east of the Heunghae area of the Pohang basin has been selected to confirm the feasibility of the Horizontal-to-Vertical Spectral Ratio (HVSR) approach to identify blind faults. Densely spaced microtremor data have been acquired along two lines in the study area and processed to obtain resonance frequencies. An empirical relationship between the resonance frequency and the bedrock depth was proposed using borehole data available in the study area. Resonance frequencies along two lines were then converted to bedrock depths. The resulting depth profiles show significant lateral variations in the bedrock depth. As expected, considerable variation in the resonance frequency is observed near the Gokgang fault. The depth profiles also present additional significant variations in the resonance frequencies and the bedrock depths. The feature is presumably related to a blind fault that is previously unknown. Therefore, this case study confirms the feasibility of the HVSR technique to identify faults otherwise not recognized on the surface.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.255-255
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• 2023

우리나라의 경우 집중호우와 돌발홍수로 인한 침수 발생에 대응하기 위해 유역 및 하천관리 사업, 각종 풍수해 예방사업 등을 추진하고 있으며, 관련 분야의 스마트기술 도입을 적극 추진하고 있다. 그러나, 2013년 노량진 상수도관 공사 현장 사고, 2019년 신월 빗물저류 배수시설 현장 사고 등과 같은 건설현장 침수 피해 사고가 지속적으로 발생하고 있다. 또한, 건설현장의 다양한 조건 및 시시각각 변화하는 상황에 따라 구조적 대책 및 대응방안을 수립하는 데 한계가 있으며 지금까지는 법, 제도에 기초한 대응 매뉴얼을 제작·배포하여 현장 근로자 교육을 실시하는 수준에서 진행되어 왔다. 본 연구에서는 건설현장의 자연재해, 특히 수재해에 대응하기 위해 보다 과학적인 방법을 통한 현장 침수 예경보 체계를 수립하였으며, 강우예측-침수예측-침수예경보 생산-현장 상황전파에 이르는 일련의 시스템을 개발하여 공사별, 규모별, 공정별 침수 대응 솔루션을 제공하고자 한다. 건설현장 침수예경보 시스템 개발의 주요 내용은 요소기술 개발이며, 간략하게 정리하면 다음과 같다. ① 강우 예측정보 생산: 현장에서 발생하는 집중호우를 고려하는 실시간 강우측정 자료와 연계한 초단기 강우예측 기술 개발, ② 침수 예측모델 개발: 현장의 시공간적 특성, 수재해 피해의 유형 등을 반영할 수 있는 침수피해 예측 모델 개발, ③ 침수예경보 의사결정 방법론 개발: 침수 피해 예경보를 위한 침수 위험단계 세분화 및 노모그래프 개발과 모델 적용(예측정확도 85% 이상), 이를 통합하여 건설현장 침수예경보 시스템 개발을 수행하게 된다. 연구에서 개발된 침수 예경보 통합 시스템은 향후 수재해로 인한 건설현장의 인명, 재산 피해 최소화에 기여할 것으로 기대된다.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.680-690
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• 2022

Tephrochronology uses regional tephra for age dating and stratigraphic correlations. Regional tephras are important in Quaternary geology and archaeology because they can be used as stratigraphic time-markers. In this review, identification and dating methods of tephra are summarized. In addition, the characteristics of regional tephras in terrestrial deposits of the Korean Peninsula are elaborated, and geological applications and limitations of the regional tephra layers are also discussed. So far, AT, Ata, and Kb-Ks tephra layers from Kyushu, Japan have been found in Pleistocene paleosol, marine terrace deposits, and lacustrine deposits in Korea. Also, although not officially confirmed, Aso-4 tephra is likely to occur in terrestrial deposits. The regional tephra layers are vital for dating, especially with regard to sediments over 50 ka beyond the range of radiocarbon dating, and for dating of active faults. Furthermore, it can provide important information for preparing countermeasures against volcanic disasters. However, in order to use the tephra layer geologically, it must be confirmed whether it is a primary deposit based on sedimentological study.

• Smart Media Journal
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• v.11 no.2
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• pp.31-38
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• 2022

Promptly predicting changes in the salinity in rivers is an important task to predict the damage to agriculture and ecosystems caused by salinity infiltration and to establish disaster prevention measures. Because machine learning(ML) methods show much less computation cost than physics-based hydraulic models, they can predict the river salinity in a relatively short time. Due to shorter training time, ML methods have been studied as a complementary technique to physics-based hydraulic model. Many studies on salinity prediction based on machine learning have been studied actively around the world, but there are few studies in South Korea. With a massive number of datasets available publicly, we evaluated the performance of various kinds of machine learning techniques that predict the salinity of the Nakdong River Estuary Basin. As a result, LightGBM algorithm shows average 0.37 in RMSE as prediction performance and 2-20 times faster learning speed than other algorithms. This indicates that machine learning techniques can be applied to predict the salinity of rivers in Korea.