우리나라는 산지가 많고 하절기에 연 강수량의 약 2/3정도가 집중적으로 발생하기 때문에 매년 산사태에 의한 피해가 발생하고 있다. 재산 및 인명을 보호하기 위해서는 사전에 산사태 발생지를 예측하고 피해를 최소화하기 위한 대책이 요구된다. 본 연구는 2020년 경상남도지역 산사태 발생지 157개소를 대상으로 붕괴면적(㎡)에 미치는 영향인자를 구명하고, 수량화이론(I)을 사용하여 붕괴면적에 대한 각 인자의 기여도 분석을 하여 예방적인 측면에서 산사태 발생 위험지역에 대한 예측기법을 개발하였다. 산사태 발생지 붕괴면적에 영향을 미치는 인자의 Range를 추정한 결과, 산사태 위험등급(0.4664)이 가장 높게 나타나 경남지역의 산사태 발생 위험도에 큰 영향을 미치는 것으로 추정되었으며, 다음으로는 영급 (0.3891), 고도(0.2934), 경급(0.2037)순으로 나타났다. 경상남도지역 산사태 발생 위험도 판정표를 기준으로 4개 인자의 category별 점수를 계산한 추정치 범위는 0점에서 1.3526점 사이에 분포하고 있으며, 중앙값은 0.6763점으로 산사태 위험도 예측을 작성한 결과 Ⅰ등급은 1.0146 이상, Ⅱ등급 0.6764∼1.0145, Ⅲ등급 0.3383∼0.6763, IV등급 0.3382 이하로 나타나 1등급, 2등급에서 산사태 발생 비율이 59.2%로서 높은 적중률을 보였다. 따라서 본 판정표는 경상남도지역에 있어서 산사태발생 위험 예측 판정에 유용하게 사용할 수 있을 것으로 사료된다.

2019년 발생한 코로나 팬데믹은 백신의 지역별 불평등 분배를 야기하며 백신의 수급은 사회･정치적 문제로 확장되어 왔다. 의료 자원에 대한 공급과 수요를 예측하고 조정하는 것은 향후 발생할 수 있는 팬데믹 위기 해결의 실마리가 된다. 본 연구는 백신이라는 한정된 의료 자원의 공간적 형평성을 달성하는 것을 목적으로, 머신러닝을 통해 미래 서울시 인구 및 공간적 백신 접근성을 예측하였다. 공간분석 분야에서 공간접근성을 측정하는 데에 통용적으로 활용되는 2SFCA(Two-Step Floating Catchment Area Method) 방법론으로 백신의 공급처인 병원의 접근성을 파악하였다. 2017년 부터 2023년까지의 백신 접근성 및 백신 취약지를 도출한 뒤, 발생 핫스팟(Emerging Hot Spot) 탐색으로 과거부터 미래까지의 분포 변화를 분석하였다. 대한민국 의료 거점지인 서울시 백신 접근성의 측정 결과, 향후 백신 접근성은 전역적으로 감소할 것으로 보이며 특히 북부지역 비롯한 외곽지역이 접근성 취약지역으로 판단되었다. 본 연구는 서울의 시공간적인 백신 공급을 예측 및 분석하여 향후 발생할 수 있을 팬데믹 상황에 대비한 백신 취약지를 보완할 수 있는 지표를 완성하였다. 연구 결과는 백신 취약지역을 효과적으로 탐색할 수 있을 뿐만 아니라 미래 효과적인 백신 분배 정책에 기초자료로 활용할 수 있을 것이라 기대한다.

This paper presents a finite-difference method (FDM)-based heat-transfer model for predicting black-ice formation on asphalt pavements and establishes decision criteria using only meteorological data. Black ice is a major cause of winter road accidents and forms under specific surface temperature and moisture conditions; however, its accurate prediction remains challenging owing to dynamic environmental interactions. The FDM incorporates thermodynamic properties, initial pavement-temperature profiles, and surface heat-transfer mechanisms, i.e., radiation, convection, and conduction. Sensitivity analysis shows the necessity of a 28-d stabilization period for reliable winter predictions. Black-ice prediction logic evaluates the surface conditions, relative humidity, wind speed, and latent-heat accumulation to assess phase changes. Field data from Nonsancheon Bridge were used for validation, where a maximum prediction accuracy of 64% is indicated in specific cases despite the overestimation of surface temperatures compared with sensor measurements. These findings highlight the challenges posed by wet surface conditions and prolonged latent-heat retention, which extend the predicted freezing duration. This study provides a theoretically grounded methodology for predicting black ice on various road structures without necessitating additional measurements. Future studies shall focus on enhancing the model by integrating vehicle-induced heat effects, solar radiation, and improved weather-prediction data while comparing the FDM with machine-learning approaches for performance optimization. The results of this study offer a foundation for developing efficient road-safety measures during winter.

PURPOSES : High temperatures induce excessive expansion in pavements, thus causing the closure of contraction joints between expansion joints. This results in the integration of slabs within the expansion joints into a unified slab. Compressive forces are generated owing to the friction that ensues between the unified slab and lower base layer. As the integrated slab expands and exceeds the allowable width of the expansion joint, the end restraint generates an additional compressive force. The escalating force, which reaches a critical threshold, induces buckling, thus compromising stability and causing blow-up incidents, which poses a significant hazard to road users. The unpredictable nature of blow-up incidents render their accurate prediction challenging because the compressive force within the slab must be predicted and the threshold for blow-up occurrence must be determined. METHODS : In this study, a GWNU blow-up model was developed to predict both the compressive force and period of blow-up incidents in jointed concrete pavements. The climate conditions, pavement structure, materials, and expansion joints were considered in this model. In the first stage of the model, the time at which the integrated slab expanded and surpassed the allowable width of the expansion joint was determined, and the compressive force was calculated. Subsequently, the compressive force within the integrated slab, considering both the end restraints and friction, was predicted. A large-scale blow-up test was performed to measure the blow-up force based on changes in the geometric imperfections. The measured blow-up force was adopted as the blow-up occurrence threshold, and the point at which the predicted compressive force within the slab exceeded the blow-up force was identified as the blow-up occurrence time. RESULTS : Using the GWNU blow-up model, the blow-up occurrence on the Seohean Expressway in Korea is predicted in the presence or absence of the alkali-silica reaction (ASR). Analysis is conducted using the expansion joint spacing and width as variables. As the expansion joint spacing increases, blow-up occurs sooner, and as the width increases, only the expansion joint life decreases. When applying an expansion joint spacing of 300 m and a width of 100 mm under an ASR with 99.9% TTPG reliability, the sum of the expansion joint life and blow-up occurrence time is 16 years. CONCLUSIONS : In the case of jointed concrete pavements where ASR occurred, installing an expansion joint spacing of 300 m and a width of 100 mm does not satisfy the design life of 20 years, and the expansion joint width minimally affect the blow-up occurrence time. To prevent blow-up incidents, a spacing of less than 300 m for the expansion joint is recommended. Based on the analysis results, the blow-up occurrence time and location can be predicted from the characteristics of the installed expansion joint, through which blow-up incidents can be prevented via preliminary maintenance.

오대산국립공원 내 뱀류 로드킬의 발생 경향 파악 및 예방을 위하여 2006-2017년 사이 공원 내에서 발생한 뱀류 로드킬 자료를 확보 및 분석하였고, 잠재적 발생지 예측을 위하여 종분포모델을 제작하였다. 연구기간 동안 뱀류 로드킬 은 600m 대의 양쪽 환경이 산림-수계인 도로에서 가장 많이 발생하였다. 모델링 결과에서 뱀류 로드킬 발생 가능성은 고도 700m 이하의 하천과의 거리가 25m 부근인 완만한 경사의 도로의 로드킬 발생확률이 높게 나타났다. 국립공원 내 주요 로드킬발생 예측지역은 국도 6호선 도로 위 공원 남쪽 경계로부터 약 2.2㎞ 지역과 약 11.7㎞ 지역이, 지방도 446호선 도로 위 공원 남쪽 경계로부터 약 3.44㎞ 지역이었다. 본 연구결과는 해발고도 700m 이하 수계와 인접한 도로 주변에 우선적으로 대체 일광욕 장소, 생태통로 및 도로의 유입을 막는 울타리의 설치가 산림에서 뱀류 로드킬을 줄이는 효과적인 방안이 될 것을 제시한다.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

본 연구에서는 당동만을 중심으로 빈산소가 발생하는 물리적 해양환경 특성을 파악하고, 로지스틱 회귀분석을 이용해 빈 산소 발생확률을 예측하였다. 관측 자료를 분석한 결과, 브런트-바이살라 주파수는 수심이 깊은 만 입구보다 수심이 얕은 만 내측에서 더 크게 나타났다. 이는 당동만 내측에서 담수 유입으로 인해 표층 염분이 낮아져 강한 밀도 성층이 형성되었기 때문이다. 시간적으로 는 6월 ~ 9월까지 리차드슨 수와 브런트 바이살라 주파수가 매우 높게 나타났고, 9월 2일 이후로는 성층이 완화되어 감소하는 경향을 보였다. 당동만에서 관측된 용존산소 및 수온, 염분 자료를 분석한 결과, 저층의 용존산소 농도는 공통적으로 표층과 저층의 수온차에 가장 큰 영향을 받는 것으로 나타났다. 한편, 수심차(dz)를 고정된 변수로 두고, 수온차(dt)의 변화에 의한 빈산소의 발생 확률의 변화 를 계산한 결과, 수심차(dz)가 각각 5 m, 10 m, 15 m, 20 m일 경우, 수온차(dt)는 8℃, 7℃, 5℃, 3℃일 때 빈산소 발생확률이 70 %를 상회 하는 것으로 나타났다. 이는 당동만에서 수심차(dz)가 커질수록 빈산소 발생에 필요한 수온차(dt)는 작아지게 된다는 것을 뜻하며, 특 히 당동만에서 수심차(dz)가 20 m 내외인 지역은 빈산소가 발생하기 매우 쉬운 환경이라는 것을 알 수 있었다.

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

채소작물과 과수작물의 생육에 악영향을 미치는 서리발생을 미리 예측하기 위해 모형을 구축하고 채소 주산지에 적용해 보았다. 서리 발생 전날에 관측되는 다양한 기상인자들(최저기온, 18시 기온, 21시 기온, 24시 기온, 평균풍속, 18시 풍속, 21시 풍속, 구름량, 5일간 강수량, 3일간 강수량, 상대습도, 이슬점온도, 초상최저기온, 지면온도)을 수집 하고, 그 중에서 서리발생에 유의한 영향이 있다고 판단되는 변수들을 통계적 방법(T-test, Random Forest, Multicollinearity test, Akaike Informaiton Criteria, 그리고 Wilk’s lambda values)을 통해 선택하였다. 여러 통계적 방법을 통해 선택된 유의한 기상 인자는 24시 기온, 구름량, 이슬점온도, 21시 풍속 이였으며, 이 기상인자를 기계학습법의 한 종류인 랜덤 포레스트에 적용하여 서리 발생 예측 모형을 구축하였다. 이렇게 구축 된 서리 발생예측 모형의 정확도는 70.6%로 나타났으며, 이 모형을 가을배추와 가을무의 주산지인 홍성과 서산에 적용하였을 때 65.2%와 78.6%로 나타났다.

The Pohang earthquake with a magnitude of 5.4 occurred on November 15, 2018. The epicenter of this earthquake located in south-east region of the Korean peninsula. Since instrumental recording for earthquake ground motions started in Korea, this earthquake caused the largest economic and life losses among past earthquakes. Korea is located in low-to moderate seismic region, so that strong motion records are very limited. Therefore, ground motions recorded during the Pohang earthquake could have valuable geological and seismological information, which are important inputs for seismic design. In this study, ground motions associated by the 2018 Pohang earthquake are generated using the point source model considering domestic geological parameters (magnitude, hypocentral distance, distancefrequency dependent decay parameter, stress drop) and site amplification calculated from ground motion data at each stations. A contour map for peak ground acceleration is constructed for ground motions generated by the Pohang earthquake using the proposed model.

Tomato leaves were inoculated with 1x104 spores · mL-1 and placed in an acryl box at 10, 15, 20, 25, and 30oC for 24 h. Ten days after inoculation, the incidence of late blight appeared as a typical symptom in 6 hrs treatment of leaf wet duration when the temperature is between 15 and 20oC at that time. The incidence of disease was 26% and 41% at 10oC and 25oC treatment although the disease did not occur even after treatment at 30oC for 16 h, respectively. The most important factors in the incidence of Late blight were leaf wet duration and temperature. Optimum growth temperature of tomato is from 15 to 25oC, thus the management of leaf wet duration is better than control by temperature to prevent the incidence of Late blight. After inoculation, the symptoms of Late blight occurred in 5 days, therefore the latency period was estimated to be 5 days. The incidence rate of Late blight was the highest at 15 and 20oC. At the time of chemicals application, when Fluopicolide 5%+Propamocarb hydrochloride 25% was applied at 12 h of leaf wet duration, the control effect was the highest as 95% at 36 h but decreased by 70% when treated after 48 h. On the other hand Cymoxanil 12% + Famoxadone 9% was applied at 18 h of leaf wet duration, the control effect was the highest as 95% at 36 h but decreased by 70% after 48 h as similar as Fluopicolide 5% +Propamocarb hydrochloride 50% treatments. In the application of Dimethomorph 15% +Dithianon 30%, the control effect was more or less low as 80% at 20 h of leaf wet duration and was decreased to 60% at 48 h.

Columns in existing reinforced concrete structures that are designed and constructed without considering seismic loads generally exhibit widely spaced transverse reinforcements without using seismic hooks. Due to the insufficient reinforcement details in columns compared to the reinforcement requirements specified in modern seismic codes, brittle shear failure is likely to occur. This may lead to sudden collapse of entire structure during earthquakes. Adequate retrofit strategy is required for these columns to avoid such catastrophic event. In order to do so, behavior of columns in existing reinforced concrete structures should be accurately predicted through computational analysis. In this study, an analytical model is proposed for accurately simulating the cyclic behavior of shear critical columns. The parameters for backbone, as well as pinching and cyclic deterioration in strength and stiffness are calibrated using test data of column specimens failed by shear.