• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.375-383
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• 2012

Soil erosion has been emphasized as serious environmental problem affecting water quality in the receiving waterbodies. Recently, Best Management Practices (BMPs) have been applied at a field to reduce soil erosion and its effectiveness in soil erosion reduction has been monitored with various methods. Although monitoring at fields/watershed outlets would be accurate way for these ends, it is not possible at some fields/watersheds due to various limitations in direct monitoring. Thus modeling has been suggested as an alternative way to evaluate effects of the BMPs. Most models, which have been used in evaluating hydrology and water quality at a watershed, could not reflect rainfall intensity in runoff generation and soil erosion processes. In addition, source codes of these models are not always public for modification/enhancement. Thus, runoff-sediment evaluation system using hourly rainfall data and vegetated filter strip (VFS) evaluation module at field level were developed using open source MapWindow GIS component in this study. This evaluation system was applied to Bangdongri, Chuncheonsi to evaluate its prediction ability and VFS module in this study. The NSE and $R^2$ values for runoff estimation were 0.86 and 0.91, respectively, and measured and simulated sediment yield were 15.2 kg and 16.5 kg indicating this system, developed in this study, can be used to simulate runoff and sediment yield with acceptable accuracies. Nine VFS scenarios were evaluated for effectiveness of soil erosion reduction. Reduction efficiency of the VFS was high when sediment inflow was small. As shown in this study, this evaluation system can be used for evaluation BMPs with local rainfall intensity and variations considered with ease-of-use GIS interface.

• Journal of Chest Surgery
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• v.31 no.8
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• pp.776-780
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• 1998

Background: In patients with coronary artery disease, dysfunctional hypoperfused myocardium at rest may represent either nonviable or viable hibernating myocardium. Two-dimensional echocardiography can detect regional wall motion abnormalities resulting from myocardial ischemia by dobutamine infusion. The purpose of the present study was to identify the prediction of improvement of regional left ventricular(LV) function after surgical revascularization. Materials and methods: Sixteen patients with chronic regional LV dysfunction underwent dobutamine stress echocardiography(DSE) (dobutamine: baseline, 5, 10, 20$\mu$g/kg/min) before coronary artery bypass grafting(CABG) and underwent echocardiography at least 2 months after CABG. Results: All patients were male with mean age of 58 years ranging from 42 to 73 years. The mean LV ejection fraction was 41.8% with a range from 19% to 55%. During DSE, there were no complications, also, there were no operative morbidities or mortalities. Improvement of wall motion within the dysfunctional myocardium was found in 8(50%) of 16 patients in DSE. Among them, 6 patients(75%) showed functional recovery after CABG. Another 8 patients did not show improvement of wall motion in DSE. But among them, 3 patients(38%) showed functional recovery after CABG. 84 dysfunctional segments were found in 256 segments of 16 patients. Improvement of wall motion was found in 34 of 84 segments in DSE. Among them, 23 segments(74%) showed functional recovery after CABG. Another 53 segments did not show improvement of wall motion in DSE. But among them, 12 segments(23%) showed functional recovery after CABG. The sensitivity and specificity of DSE for the prediction of postoperative improvement of segmental wall motion were 66% and 84%, respectively. The positive and negative predictive value of DSE were 74% and 77%, respectively. In patients with chronic regional LV dysfunction, think that DSE is a good predictor of the improvement of dysfunctional segments after CABG.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.233-239
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• 2014

This study was carried out to propose the criteria for the prediction of landslide occurrence through analysis the influence of each factor by using the quantification theory. The results obtained from this study are summarized as follows. From a stepwise regression analysis between the landslide area($m^2$) and environmental factors, the factors strongly affecting the landslide sediment($m^2$) were the Parents rock (igneous), cross slope(complex), coniferous forests (forest type) and slope gradient ($21{\sim}30^{\circ}$). According to the range, it was shown in order of Cross slope (0.2922), Parents rock (0.2691), Forest type (0.2631) and Slope gradient (0.2312). The range of prediction score of landslide occurrence has been distributed between score 0 and score 1.0556, the median value was score 0.5278. The prediction for class I was over 0.7818, for class II was 0.5279 to 0.7917, for class III 0.2694 to 0.5278 and for class IV was below 0.2693. The prediction on landslide occurrence appeared relatively high accuracy rate as 72% for class I and II. Therefore, this score table for landslide will be very useful for judgement of dangerous slope.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.67-72
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• 2006

Temperature prediction in asphalt pavements is the one of most important factors for estimating the pavement response and predicting the pavement performance in the mechanistic-empirical pavement design. A study on temperature prediction procedure with variation of time and depth in asphalt pavements was conducted using field monitoring data. After selecting the temperature monitoring sections, the temperature sensors have been installed in different depths and the temperature data have been collected in every one hour. The developed pavement temperature prediction model was calibrated using field monitoring temperature data. The predicted temperatures were compared with measured temperatures at different seasons in selected sections. The results showed that the solar absorptivity and emissivity values in the fall is different from the values in other seasons. The predicted temperatures agree well with the measured temperatures at a wide range of temperatures. The temperature differences between each other fall in the range of ${\pm}3^{\circ}C$. It is also found that the regional characteristics did not affect the temperature prediction procedure.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Journal of the Korean GEO-environmental Society
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• v.16 no.2
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• pp.5-13
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• 2015

In the present study, the prediction methods of the crest settlement after impounding and the maximum internal settlement during dam construction were proposed through the analysis on settlement data at 38 monitored points of 36 Concrete-Faced Rockfill Dams (CFRDs). The results from this analysis provided that the crest settlement and the maximum internal settlement are increased in proportion to the dam height and the void ratio. However, the relationship between internal settlement and dam height for each void-ratio range plotted in semi-logarithmic scale is the nearly same. Also, the prediction of the crest settlement of the CFRD is possible through the maximum internal settlement during dam construction. In addition, it seems that the valley shape highly affects the dense dam body with high construction modulus. The results of this study will provide the useful tool for the design, construction and management of CFRDs.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.21-30
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• 2020

In this paper, we predicted the propagation loss for the air-to-ground (A2G) channel between the ground control system and the unmanned aerial vehicle (UAV) using the prediction model for the aircraft recommended by the International Telecommunication Union (ITU). We analyzed the network coverage of the aerial relay system based on the medium altitude UAVs by expanding it into the air-to-air (A2A) channel. Climate and geographic factors in Korea were used to predict propagation loss due to atmospheres. We used the measured data published by the Telecommunication Technology Association (TTA) for regional rainfall-rate and effective earth radius factors to increase accuracy. In addition, the aerial relay communication system used the key parameter of the common data link (CDL) system developed in Korea recently. Prediction results show that the network coverage of the aerial relay system broadens at higher altitude.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.587-596
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• 2002

A super ensemble model was developed for the seasonal prediction of regional precipitation in Korea using the lag correlated large scale predictors, based on the empirical orthogonal function (EOF) analysis and multiple linear regression model. The predictability of this model was also evaluated by cross-validation. Correlation between the predicted and the observed value obtained from the super ensemble model showed 0.73 in spring, 0.61 in summer, 0.69 in autumn and 0.75 in winter. The predictability of categorical forecasting was also evaluated based on the three classes such as above normal, near normal and below normal that are clearly defined in terms of a priori specified by threshold values. Categorical forecasting by the super ensemble model has a hit rate with a range from 0.42 to 0.74 in seasonal precipitation.

• 한국지역지리학회:학술대회
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• 2009.08a
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• pp.108-115
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• 2009

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmentaldescription of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapor Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapor Explosion), Fireball and so on, among them.we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.