• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.59-66
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• 2016

The 2011 debris flow in Mt. Umyeonsan in Seoul, South Korea caused significant damages to the surrounding urban area, unlike other similar incidents reported to have occurred in the past in the country's mountainous regions. Accordingly, landslides and debris flows cause damage in various surroundings, regardless of mountainous area and urban area, at a great speed and with enormous impact. Hence, many researchers attempted to forecast the extent of impact of debris flows to help minimize the damage. The most fundamental part in forecasting the impact extent of debris flow is to understand the debris flow behavior and sedimentation mechanism in complex three-dimensional topography. To understand sedimentation mechanism, in particular, it is necessary to calculate the amount of energy and erosion according to debris flow behavior. The previously developed debris flow models, however, are limited in their ability to calculate the erosion amount of debris flow. This study calculated the extent of damage caused by a massive debris flow that occurred in 2011 in Seoul's urban area adjacent to Mt. Umyeonsan by using DEM, created from aerial photography and airborne LiDAR data, for both before and after the damage; and developed and compared a debris flow behavioral analysis model that can assess the amount of erosion based on energy theory. In addition, simulations using the existing debris flow model (RWM, Debris 2D) and a comprehensive comparison of debris flow-stricken areas were performed in the same study area.

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

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.767-776
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• 2014

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Floods are one of the most deadly and damaging natural disasters known to mankind. The flood forecasting and warning system concentrates on reducing injuries, deaths, and property damage caused by floods. Therefore, the exact relationship and the spatial variability analysis of hydrometeorological elements and characteristic factors is critical elements to reduce the uncertainty in rainfall-runoff model. In this study, grid resolution depending on the topographic factor in rainfall-runoff models presents how to respond. semi-distribution of rainfall-runoff model using the model GRM simulated and calibrated rainfall-runoff in the Gamcheon and Naeseongcheon watershed. To run the GRM model, input grid data used rainfall (two event), DEM, landuse and soil. This study selected cell size of 500 m(basic), 1 km, 2 km, 5 km, 10 km and 12 km. According to the resolution of each grid, in order to compare simulation results, the runoff hydrograph has been made and the runoff has also been simulated. As a result, runoff volume and peak discharge which simulated cell size of DEM 500 m~12 km were continuously reduced. that results showed decrease tendency. However, input grid data except for DEM have not contributed increase or decrease runoff tendency. These results showed that the more increased cell size of DEM make the more decreased slope value because of the increased horizontal distance.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.655-666
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• 2013

Lately, intensity and frequency of natural disasters such as flood are increasing because of abnormal climate. Casualties and property damages due to large-scale floods such as Typhoon Rusa in 2002 and Typhoon Maemi in 2003 rapidly increased, and these show the limits of the existing disaster prevention measures and flood forecasting systems regarding irregular climate changes. In order to efficiently respond to extraordinary flood, it is important to provide effective countermeasures through an inundation model that can accurately simulate flood inundation patterns. However, the existing flood inundation analysis model has problems such as excessive take of analysis time and accuracy of the analyzed results. Therefore, this study conducted a flood inundation analysis by using the Gerris flow solver that uses quadtree grid, targeting the Baeksan Levee in the Nakdong River Basin that collapsed because of a concentrated torrential rainfall in August, 2002. Through comparisons with the FLUMEN model that uses unstructured grid among the existing flood inundation models and the actual flooded areas, it determined the applicability and efficiency of the quadtree grid-based flood inundation model of the Gerris flow solver.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.101-112
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• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• The Journal of Sustainable Design and Educational Environment Research
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• v.15 no.1
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• pp.30-39
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• 2016

Students receiving plan is not based on short-term indicators, such as student-centered, student-induced factor to address school needs new complaint, it is necessary to establish the school in the center of a long-term (30 years) perspective. Therefore, analysis of Cheongju students can examine the entire 30 years of the elementary school in this study are as follows: First, given the increasing number of students in seven models and presented the case to its types. Second, considering the geographical characteristics and the development of regional characteristics classify 55 elementary Schools in Cheongju City by dividing the number of students increase or decrease trend to 10 zones the results are as follows: Students Number increasing school group of 4 schools, 15 schools students Number fell in shot Term, the Students Number dropped in middle Term 26 schools, 10 was a small school. In particular, it is urgently necessary to establish measures for these small schools. Third, despite the reduced number of students indicated in the analysis result, caused the social conflict factors by excessive new school requirements. It also caused a number of students from schools when the Curve of Students Number are to remain flat or decline. It shows that no additional new demand of School in the region. Fourth, the number of students increasing trend forecasting model

• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.61-87
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• 2010

This study is based on the analysis of technology innovation performance for Inno-biz. The primary purposes of this study are to help the government formulate Inno-biz related supporting policies and improve the fitness of evaluation models for Inno-biz. In this study the definition of "the outcome of technology innovation" includes technology competitiveness changes, technology forecasting as well as the outcome of technology innovation. For this analysis, 55 independent variables were used and categorized into ability of technology innovation, ability of commercialization, and ability of technology management. The results indicate that all three variable groups have positively influenced the outcome of technology innovation. Especially ability of technology innovation is highly related to technology competitiveness and business in future. The ability of commercialization enhances technology competitiveness and predictability in major business indexes; however it doesn't influence business performance in a short-term period. The ability of technology management enables businesses to forecast technology changes, but doesn't effect short-term business outcomes.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.4
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• pp.52-65
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• 1984

In general precise estimation of hourly of daily distribution of the long-term run-off should be very important in a design of source of irrigation. However, there have not been a satisfying method for forecasting of stationar'y long-term run-off in Korea. Solving this problem, this study introduces unit-hydrograph method frequently used in short-term run-off analysis into the long-term run-off analysis, of which model basin was selected to be Sumgin-river catchment area. In the estimation of effective rainfall, conventional method neglects the Soil moisture condition of catchment area, but in this study, the initial discharge (qb) occurred just before rising phase of the hydrograph was selected as the index of a basin soil moisture condition and then introduced as 3rd variable in the analysis of the reationship between cumulative rainfall and cumulative loss of rainfall, which built a new type of separation method of effective rainfall. In next step, in order to normalize significant potential error included in hydrological data, especially in vast catchment area, Snyder's correlation method was applied. A key to solution in this study is multiple correlation method or multiple regressional analysis, which is primarily based on the method of least squres and which is solved by the form of systems of linear equations. And for verification of the change of characteristics of unit hydrograph according to the variation of a various kind of hydrological charateristics (for example, precipitation, tree cover, soil condition, etc),seasonal unit hydrograph models of dry season(autumn, winter), semi-dry season (spring), rainy season (summer) were made respectively. The results obtained in this study were summarized as follows; 1.During the test period of 1966-1971, effective rainfall was estimated for the total 114 run-off hydrograph. From this estimation results, relative error of estimation to the ovservation value was 6%, -which is mush smaller than 12% of the error of conventional method. 2.During the test period, daily distribution of long-term run-off discharge was estimated by the unit hydrograph model. From this estimation results, relative error of estimation by the application of standard unit hydrograph model was 12%. When estimating by each seasonal unit bydrograph model, the relative error was 14% during dry season 10% during semi-dry season and 7% during rainy season, which is much smaller than 37% of conventional method. Summing up the analysis results obtained above, it is convinced that qb-index method of this study for the estimation of effective rainfall be preciser than any other method developed before. Because even recently no method has been developed for the estimation of daily distribution of long-term run-off dicharge, therefore estimation value by unit hydrograph model was only compared with that due to kaziyama method which estimates monthly run-off discharge. However this method due to this study turns out to have high accuracy. If specially mentioned from the results of this study, there is no need to use each seasonal unit hydrograph model separately except the case of semi-dry season. The author hopes to analyze the latter case in future sudies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.133-144
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• 2017

Recently, weather information is getting closer to our real life, and it is a very important factor especially in the transportation field. Although the damage caused by the abnormal climate changes around the world has been gradually increased and the correlation between the road risk and the possibility of traffic accidents is very high, the domestic research has been performed at the level of basic research. The Purpose of this study is to develop a risk map for the road hazard forecasting service of weather situation by linking real - time weather information and traffic information based on accident analysis data by weather factors. So, we have developed a collection and analysis about related data, processing, applying prediction models in various weather conditions and a method to provide the road hazard map for national highways and provincial roads on a web map. As a result, the road hazard map proposed in this study can be expected to be useful for road managers and users through online and mobile services in the future. In addition, information that can support safe autonomous driving by continuously archiving and providing a risk map database so as to anticipate and preemptively prepare for the risk due to meteorological factors in the autonomous driving vehicle, which is a key factor of the 4th Industrial Revolution, and this map can be expected to be fully utilized.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.