• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.4
• /
• pp.33-43
• /
• 2020

In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundation accurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of Korea Meteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it is necessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is to establish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts was collected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfall was performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculated by extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundation probability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage from inundation.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.6
• /
• pp.1873-1879
• /
• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Management & Information Systems Review
• /
• v.35 no.2
• /
• pp.221-254
• /
• 2016

With increasing interest of health due to population aging, medical device industry is highlighted as a promising industry. However, Korea medical device industry is not enough market competitiveness compared to global company due to a narrow domestic market and a small company structure. In order to retain the national competitiveness, it is necessary that we have to derive a central technology and its trend. This study has predicted a central technology for medical device industrial using patent network analysis. The central technology is defined as a key technology that is connected to most other technologies and that significantly affects them. For the empirical study, we conducted social network analysis using covariance and correlation coefficient between IPC codes extracted from medical device patents, introduced by Jun(2012). A social network is a social structure of diverse items as well as of human beings. In this study, we set each medical device as a node in an SNA and analyze the Degree values between them. Also, Korea health industrial statistics system are utilized for verification of selected central technology. As a result, we found that the central technology is located on the medical device items, which are listed higher the amount of production. The central technology selected through the proposed methodology will provide a inspiration for establishment of R&D policy.

• Wind and Structures
• /
• v.30 no.4
• /
• pp.433-450
• /
• 2020

The strong turbulence characteristic of typhoon not only will significantly change flow field characteristics surrounding the large-scale wind turbine and aerodynamic force distribution on surface, but also may cause morphological evolution of coast dune and thereby form sand storms. A 5MW horizontal-axis wind turbine in a wind power plant of southeastern coastal areas in China was chosen to investigate the distribution law of additional loads caused by wind-sand coupling movement of coast dune at landing of strong typhoons. Firstly, a mesoscale Weather Research and Forecasting (WRF) mode was introduced in for high spatial resolution simulation of typhoon "Megi". Wind speed profile on the boundary layer of typhoon was gained through fitting based on nonlinear least squares and then it was integrated into the user-defined function (UDF) as an entry condition of small-scaled CFD numerical simulation. On this basis, a synchronous iterative modeling of wind field and sand particle combination was carried out by using a continuous phase and discrete phase. Influencing laws of typhoon and normal wind on moving characteristics of sand particles, equivalent pressure distribution mode of structural surface and characteristics of lift resistance coefficient were compared. Results demonstrated that: Compared with normal wind, mesoscale typhoon intensifies the 3D aerodynamic distribution mode on structural surface of wind turbine significantly. Different from wind loads, sand loads mainly impact on 30° ranges at two sides of the lower windward region on the tower. The ratio between sand loads and wind load reaches 3.937% and the maximum sand pressure coefficient is 0.09. The coupling impact effect of strong typhoon and large sand particles is more significant, in which the resistance coefficient of tower is increased by 9.80% to the maximum extent. The maximum resistance coefficient in typhoon field is 13.79% higher than that in the normal wind field.

• Journal of Korea Water Resources Association
• /
• v.43 no.3
• /
• pp.295-308
• /
• 2010

As an attempt to explore the impact of droughts which may be worse by the climate change, the change in the water balance of the Han-river basin is analyzed. To accomplish it, we suggest a procedure consisting of three successive sub-procedures: daily rainfall generation for 70 years by the RegCM3 RCM ($27{\times}27\;km$) with the A2 scenario, daily discharge simulations by SLURP using the generated daily rainfall data, and monthly water balance analysis by K-WEAP (Korean Water Evaluation and Planning System) based on the SLURP simulation. Since significant uncertainty is involved in forecasting the future water consumption and water yields, we assumed three water consumption scenarios and fifty water yields scenarios. Three water consumption scenarios are, namely, "LOW", "MEDIUM", and "HIGH" according to the expected amount of water consumption. The fifty daily discharges are obtained from the SLURP simulations during the drought period. Finally, water balance analysis is performed by K-WEAP based on 150 combinations from three water consumption scenarios and the fifty daily discharges. Analysis of water scarcity in small basins of the Han River basin showed concentration of water scarcity in some small basins. It was also found that water scarcity would increase in all small basins of the Han River basin.

• Journal of Korean Society of Transportation
• /
• v.31 no.5
• /
• pp.37-47
• /
• 2013

This Study analyzed the electric car effect on the Korea Expressway System in terms of year 2020 $CO_2$ emission. The analysis was based on the green car dissemination goal by the government and year 2010 emission statistics. Major contents performed in the study area were as follows. First, the greenhouse gases emitted from the highways were found to be approximately 17.3 million tons of $CO_2$ as of 2010. Analysis showed the emission would be 17.4 million tons in 2015 and 16.2 million tons in 2020. The results in the pattern reflect the effect of O/D on the KTBD and the trend of traffic increase from 2015 to 2020 followed by decrease in 2020. Second, in the case of greenhouse gas emission with the anticipated supply of electric cars, the amount of emission in 2015 will be 17.1 million tons, which is about 2.0% reduction compared to the lack of introduction of electric cars. The analysis also showed that in 2020, the amount of emission will be 14.2 million tons, which indicates the effect of reduction is 12.8% compared to non implementation of the program.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.4
• /
• pp.73-85
• /
• 2014

Approaching to aging society with increasing transportation vulnerable, most developed countries has positively promote low-floor bus. Such circumstance in Korea has plan to introduce low-floor bus to intra-city bus system which accounted for 30 percent of total number of buses however there is no specific operating plan for this matter. According to the revealed preference study on bus service, the study shows that the efficiency of low-floor is relatively low than that of other buses, therefore, it is necessary to establish feasible plan for bus route selection. Thus, this study is to conduct research on analyzing trip characteristics of transportation vulnerable and establish bus route selection measures for low-floor bus. The result from the survey in Jeollabuk-do Province reveals that the trip purpose of transportation vulnerable is mainly for welfare and medical service, which was made less than 6 times a week. Futhermore, 37.6 percent of transportation vulnerable use buses, thus, it is essential to improve its service quality for enhancing user's convenience and safety. In that transportation vulnerable O-D needs to be established and forecasts future demand for selecting optimal bus route. According to the estimation, route passing through densely populated areas with transportation vulnerable should take the first priority, city circular and other route would be next. Moreover, it is economically efficient that areas populated more than 200,000 with fixed route and less than 200,000 with limited route responsive to demands would be feasible plans. This study will have greater an impact on transportation planning and further research on transportation vulnerable.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.82-82
• /
• 2016

가뭄은 장기간에 걸쳐 기상, 수문, 유역조건 등 복잡하고 다양한 요소에 의해 영향을 받아 진행되므로 사전에 인지하고 판단하는 것은 매우 어렵다. 또한, 일단 가뭄이 진행되면 사회 경제적으로 피해가 막대하게 발생할 수 있기 때문에 가뭄이 발생하기 전 효율적으로 모니터링하고 사전에 대응하는 것이 무엇보다도 중요하다. 이러한 가뭄의 피해를 최소화하기 위해 미국에서는 20년 전부터 가뭄감시 및 조기경보를 통한 선제적 가뭄대응 체계를 구축하여 운영 중이지만 우리나라는 그간 '사후피해 최소화' 위주의 정책으로 가뭄에 대응해 왔다. 지난해 우리나라를 포함한 전 세계는 역사상 유례없는 강수량 부족에 따른 가뭄으로 많은 어려움을 겪었다. 이에 따라, 정부는 보다 현실성 있는 가뭄분석과 대응을 목적으로 지난해 11월 국가정책조정회의를 통해 '선제적 가뭄대응'을 위한 가뭄정보분석센터(이하 센터)를 K-water에 신설하였다. 그간 우리나라는 기상청에서 가뭄을 판단하고 예측하기 위해 강수량 또는 토양수분량 등을 활용하는 '가뭄지수'를 통해 가뭄에 대한 정보를 일부 제공해 왔다. 하지만 국민들은 생 공용수 부족 시 가뭄을 체감하게 되므로 '가뭄지수'에 근거한 가뭄 판단으로는 국민이 체감하는 가뭄을 제대로 표현하지 못하는 한계가 있었다. 따라서 '가뭄지수'에 근거한 가뭄 판단으로는 초기 가뭄대응 시 국민적 공감을 얻지 못할 뿐만 아니라 효율적으로 가뭄에 대응하지 못하는 결과까지 초래될 수 있어 우리나라 실정에 맞는 가뭄판단기준과 전망기준마련이 무엇보다 시급하다고 할 수 있다. 센터는 이러한 기존 '가뭄지수'가 갖는 한계를 극복하기 위해 생 공용수의 수급 불균형을 고려하여 우리나라 실정에 맞는 수원별 가뭄판단기준과 전망기준을 마련하였으며, 이를 기반으로 1월에는 충청 및 수도권 지역에 대한 가뭄정보분석시스템을 구축하였고, 3월부터는 전국단위로 확대해 가뭄 예 경보를 시범운영 중에 있다. 또한 센터는 1년동안 시범운영 기간을 거친 후 내년부터는 본격적인 대국민 가뭄 모니터링 및 전망 정보서비스를 제공할 예정이다. 향후에는 위성정보를 활용한 가뭄영향 평가와 가뭄에 따른 물환경 영향 평가 등으로 영역을 확장하여 가뭄 통합정보를 제공하고, 사회적 경제적 영향이 고려된 가뭄평가뿐만 아니라 물리적 기반의 정량적 예측을 지속적으로 추진하여 기후변화에 대응하고 국민과 함께 가뭄문제를 효과적 해결할 수 있도록 노력해 나갈 예정이다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.5
• /
• pp.605-612
• /
• 2019

Traditionally, travel demand forecasts have been conducted based on the data collected by a survey of individual travel behavior, and their limitations such as the accuracy of travel demand forecasts have been also raised. In recent, advancements in information and communication technologies are enabling new datasets in travel demand forecasting research. Such datasets include data from global positioning system (GPS) devices, data from mobile phone signalling, and data from call detail record (CDR), and they are used for reducing the errors in travel demand forecasts. Based on these background, the objective of this study is to assess the feasibility of CDR as a base data for travel demand forecasts. To perform this objective, CDR data collected for Daegu Metropolitan area for four days in April including weekdays and weekend days, 2017, were used. Based on these data, we analyzed the correlation between CDR and travel demand by travel survey data. The result showed that there exists the correlation and the correlation tends to be higher in discretionary trips such as non-home based business, non-home based shopping, and non-home based other trips.

• Journal of Korea Water Resources Association
• /
• v.54 no.12
• /
• pp.1285-1294
• /
• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.