• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.209-209
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• 2023

본 연구에서는 강우 시 발생하는 음향 및 진동 신호를 기반으로 강우강도를 산정하기 위한 방법론을 제안하였다. 먼저, Raspberry Pi, 콘덴서 마이크 및 가속도 센서로 구성된 관측 기기로부터 실제 비가 내리는 환경에서의 음향 및 진동 신호를 수집하였다. 가속도 센서로부터 계측된 진동 신호를 활용하여 강우 유무에 대한 이진 분류를 수행하고, 강우가 발생한 것으로 판단된 기간에 해당하는 음향 신호에 Short-Time Fourier Transform 기술을 적용하여 주파수 영역에서 나타나는 magnitude의 평균과 표준 편차, 최고 주파수 등의 특징을 기반으로 강우강도를 산정하였다. 이를 위해 앙상블 기반의 머신러닝 학습 모델인 XGBoost 알고리즘을 사용하였으며, 광학 우적계를 통해 관측한 강우강도와 산정 결과를 비교·평가하였다. 강우강도 산정 과정에서 사용된 음향 신호의 길이를 1초, 10초, 1분으로 구분하였으며, 무강우 기간 내 음향 정보로부터 배경 음향에 의한 노이즈를 제거하고자 하였다. 최종적으로 강우 유무 이진 분류 과정의 선행 여부, 음향 신호의 길이 및 노이즈 제거 방법에 따른 강우강도 산정 결과들에 대한 성능 비교를 통해 본 연구에서 제안하고자 하는 방법론의 실효성을 평가하였다.

• Journal of Korean Society of Disaster and Security
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• v.6 no.2
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• pp.23-30
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• 2013

A dynamic analysis of random vibration processes is concerned with the first excursion probability based on first passage time during some specified lifetime or duration of the excitation. This study is concerned with the estimation of first-passage probability for hazard fluctuate wind velocity in the major cities reflecting the recent meteorological with largest data samples (yearly 2003-2012). The basic wind speeds were standardized homogeneously to the surface roughness category C, and to 10m above the ground surface. In this paper, the hazard fluctuate wind velocities are treated as a time-independent (stationary) random process and Gaussian random processes. The first excursion probability were calculated from Poisson model based on the independent event of level crossing & two-state Markov model based on the envelopes of level crossing.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.156-156
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• 2016

2016년 2월 5일, 짐바브웨는 극심한 가뭄으로 인해 인구의 4분의 1이상이 식량난을 겪고 있다며 '국가 재난 사태'를 선포하였다. 한때 아프리카 곡창지대로 불리던 짐바브웨가 극심한 가뭄을 겪게 된 데에는 2015/16년 슈퍼엘니뇨의 영향이 크게 한 몫을 하였는데, 이는 남반구의 여름인 11월부터 이듬해 3월까지인 짐바브웨의 우기가 2015/16년 슈퍼엘니뇨 강도가 절정에 달했던 시기(10월에서 2월)와 겹쳐져 짐바브웨의 강수량이 슈퍼 엘니뇨의 영향을 받게 되었기 때문이다. 게다가 4월부터는 엘니뇨의 영향을 받은 우기가 끝나고 건기가 시작되기 때문에 앞으로 가뭄이 얼마나 더 악화될지 우려되는 상황이다. 짐바브웨의 기후를 살펴보면, 증발량이 강수량보다 많은 건조기후 중에서도 비교적 그 정도가 약한 기후인 반건조 지대에 속한다. 하지만 연강수량 변동에 따라서, 비가 내리는 해에는 토양 수분이 과잉되고 비가 적게 내리는 해에는 심한 물 부족 현상이 일어나게 되기 때문에, 건기가 시작되는 4월부터 짐바브웨 강수 예측은 가뭄이 얼마나 지속될지를 파악하는 데에 아주 중요한 요소가 될 수 있다. 따라서 본 연구에서는 강수 예측 결과를 중심으로 2016년 짐바브웨의 가뭄이 얼마나 지속되고, 또 가뭄의 강도는 어떻게 될지 알아보는 것에 목적을 두고, GCM을 이용하여 2016년 3월에서 10월까지 장기예측을 수행하였다. 경계 자료로는 ECMWF (European Centre for Medium Range Weather Forecasts)에서 제공하는 Sea Ice자료와, NOAA OI (National Oceanic and Atmospheric Administration Optimum Interpolation) Weekly SST자료를 사용하였고 엘니뇨의 영향을 고려하기 위해 IRI (International Research Institute)의 ENSO forecast를 참고하여 SST아노말리에 월별 가중치를 적용하였다. 초기 입력 자료로는 1월 21-30일 10일간의 ECMWF의 재분석 자료를 이용하여 총 10개 멤버의 앙상블 예측을 수행하였고, 8개월(3-10월) 기간에 대해 약 한 달간의 spin-up time을 주었다. 예측 자료를 모델 climatology와 비교하여 월 평균 강수 전망을 분석하였고, 기온과 해면기압의 월 평균자료도 추가 분석하였다. 또한 짐바브웨 지역의 강수 관측 자료와 모델 예측 자료를 이용하여 특정 도시들의 1년 누적강수를 예측 및 분석하였고, 최종적으로 이 결과를 통해 짐바브웨의 가뭄지속가능성을 살펴보았다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.51-51
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• 2017

지역빈도해석은 짧은 기간의 자료를 보유하고 있는 계측 지점이나 자료가 없는 미계측 지점에서의 확률수문량을 산정하기 위하여 많이 쓰여 진다. 지역빈도해석을 실시하기 위한 조건으로는 우선 수집된 하천유역들을 대상으로 수문학적 동질 지역을 구분하는 것이 중요하다. 그리고 구분되어진 지역에 포함되는 모든 지점들의 자료를 빈도해석 함으로써 관심 지점의 신뢰할 만한 확률수문량을 산정하는 것이다. 그동안의 지역빈도해석은 주로 비건조지역을 중심으로 홍수와 같은 재난재해 대비 그리고 수자원 관리를 위한 연구들을 실시해왔다. 본 연구의 주 목적은 건조지역의 수자원 관리를 위해 건조지역 하천유역을 중심으로 지역빈도해석을 실시하여 신뢰할만한 확률수문량을 산정하는 것이다. 확률수문량 산정값의 정확도를 향상시키기 위해 지역빈도해석 모델에 쓰여 지는 새로운 지형학적 변수들을 제공하였고 수문학적 동질 지역을 구분 위해 수집된 각 하천유역의 형상들을 확인하여 동질 지역을 정의하였다. 예를 들면, 수지형 유역, 부채형 유역, 격자형 유역과 같은 다른 형상들을 구분하여 각 유역 형상 종류별로 동질 지역을 만들었다. 건조지역의 지역빈도해석을 위해 미국 건조지역의 105개 하천유역 유량자료들을 수집 및 이용하였다. 확률수문량 산정을 위하여 앙상블 인경신경망 (Ensemble Artificial Neural Network)과 정준 상관 계수(Canonical Correlation Analysis)를 이용한 지역빈도해석 모델을 만들었다. 제안된 모델의 수행평가와 정확성 평가를 위해 리샘플링 기법인 10-겹 교차 검증 (10-fold cross-validation), 잭나이프 (Jackknife) 기법들을 이용하였고 모델로부터 산정된 확률수문량값을 편향 (Bias), 상대 편향(rBias), 평균 제곱근 오차 (RMSE), 상대 평균 제곱근 오차 (rRMSE)를 통하여 산정 값과 실제 관측 값의 차이를 분석하였다. 그 결과 건조지역의 지역빈도해석을 위해 새롭게 제시된 지형학적 변수들을 사용하였을 때 모델의 수행능력이 향상되었음을 확인하였다. 또한 하천유역 형상에 따라 동질 지역을 구분하였을 때 향상된 확률수문량이 산정되었다. 향상된 지역빈도해석 모델을 통해 건조지역의 신뢰할만한 확률수문량을 산정함으로써 건조지역의 효과적인 수자원 관리를 위한 수공시설물 설계에 중요한 정보들을 제공할 것이다.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.313-327
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• 2013

In the study, three-dimensional geostatistical simulations on McMurray Formation which is the largest oil sand reservoir in Athabasca area, Canada were performed, and the optimal site for steam assisted gravity drainage (SAGD) was selected based on the predictions. In the selection, the factors related to the vertical extendibility of steam chamber were considered as the criteria for an optimal site. For the predictions, 110 borehole data acquired from the study area were analyzed in the Markovian transition probability (TP) framework and three-dimensional distributions of the composing media were predicted stochastically through an existing TP based geostatistical model. The potential of a specific medium at a position within the prediction domain was estimated from the ensemble probability based on the multiple realizations. From the ensemble map, the cumulative thickness of the permeable media (i.e. Breccia and Sand) was analyzed and the locations with the highest potential for SAGD applications were delineated. As a supportive criterion for an optimal SAGD site, mean vertical extension of a unit permeable media was also delineated through transition rate based computations. The mean vertical extension of a permeable media show rough agreement with the cumulative thickness in their general distribution. However, the distributions show distinctive disagreement at a few locations where the cumulative thickness was higher due to highly alternating juxtaposition of the permeable and the less permeable media. This observation implies that the cumulative thickness alone may not be a sufficient criterion for an optimal SAGD site and the mean vertical extension of the permeable media needs to be jointly considered for the sound selections.

• Korean journal of applied entomology
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• v.62 no.3
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• pp.215-220
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• 2023

A recent discovery of drywood termites (Cryptotermes domesticus) in a residential facility in Seoul has raised significant concern. This exotic insect species, which can damage timber and wooden buildings, necessitates an immediate investigation of potential infestation. In this study, we assessed the climatic suitability for this termite species using a species distribution modeling approach. Global distribution data and bioclimatic variables were compiled from published sources, and predictive models for climatic suitability were developed using four modeling algorithms. An ensemble prediction was made based on the mean occurrence probability derived from the individual models. The final model suggested that this species could potentially establish itself in tropical coastal regions. While the climatic suitability in South Korea was generally found to be low, a careful investigation is still warranted due to the potential risk of colonization and establishment of this species.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.209-218
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• 2024

The National Institute of Agricultural Sciences (NAS) operates in-house long-range climate forecasting system to support the agricultural use of climate forecast data. This system, developed through collaborative research with Pusan National University, is based on the PNU/RDA Coupled General Circulation Model (CGCM) and includes the regional climate model WRF (Weather Research and Forecasting). It generates detailed climate forecast data for periods ranging from 1 to 6 months, covering 20 key variables such as daily maximum, minimum, and average temperatures, precipitation, and agricultural meteorological elements like solar radiation, soil moisture, and ground temperature-factors essential for agricultural forecasting. The data are provided at a daily temporal resolution with a spatial resolution of a 5km grid, which can be used in point form (interpolated) or averaged across administrative regions. The system's seasonal temperature and precipitation forecasts align closely with observed climatological data, accurately reflecting spatial and topographical influences, confirming its reliability. These long-range forecasts from NAS are expected to offer valuable insights for agricultural planning and decision-making. The detailed forecast data can be accessed through the Climate Change Assessment Division of NAS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.413-422
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• 2012

In this study, we developed the severity-adjusted length of stay (LOS) model for acute myocardial infarction patients using data from the hospital discharge survey and proposed management of medical quality and development of policy. The dataset was taken from 2,309 database of the hospital discharge survey from 2004 to 2006. The severity-adjusted LOS model for the acute myocardial infarction (AMI) patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of AMI patients were CABG and comorbidity. The difference between severity-adjusted LOS from the ensemble model and real LOS was compared and it was confirmed that insurance type and location of hospital were statistically associated with LOS. And to conclude, hospitals should develop the severity-adjusted LOS model for frequent diseases to manage LOS variations efficiently and apply it into the medical information system.