• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.352-365
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• 2012

A primary study on the rapid modeling of storm surge, which is one of typical coastal disasters, for immediate forecasting in conjunction with typhoon advisories is done and tested for the typhoons Bolaven, Tembin and Sanba which attacked to Korean Peninsula on August and September in this year 2012. Semi automatic rapid computations according to JTWC typhoon advisories were performed and uploaded to the web by models SLOSH in PC and ADCIRC in parallel clusters with 64 cores having 57k nodes encompassing the North-Western Pacific region. It only takes 1 and 2 hours from taking advisory to web uploading, respectively. By comparison with observed water surface elevations for the major tidal stations after Bolaven attack it shows within RMS error of 0.17~0.19 m for surge heights and only deviates 1 hour of peak surge time in ADCIRC model. Thus it is concluded that this approach provides a frame of near real-time immediate forecasting of storm surges with satisfactions.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.13-23
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• 2011

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.110-117
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• 2015

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.1
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• pp.79-98
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• 1999

There have been a number of empirical studios on the effectiveness of Judgmental adjustment to statistical forecasts Generally the results have been mixed. This study examined the impact of the reliability and the source of the additionally presented reference forecast upon the revision process in a longitudinal time series forecasting task with forecast support systems. A 2-between(reliability ＆ source). 2-within(seasonality ＆ block) factorial experiment was conducted with post-graduate students using real time series. Judgmental adjustment was found to improve the accuracy of initial eyeballing irrespective of the reliability of an additionally presented forecast. But it did not outperform the dampened reference forecast. No effect was found of the way the source of the reference forecast was framed. Overall the subjects anchored heavily on their Initial forecast and relied too little on the reference forecast irrespective of its reliability. Moreover they did not improve at the task over time, despite immediate outcome feedback.

• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.539-547
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• 2018

Objectives: Ozone ($O_3$) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high $O_3$ concentrations cannot be considered ideal measures. Forecasts of $O_3$ would be more beneficial to citizens' health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface $O_3$ concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan's air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between $NO_2$ and $O_3$ was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for $O_3$ and $NO_x$ were also found. Our model suggests that insolation is the most influential factor in determining the concentration of $O_3$. $CH_4$ also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted $O_3$ concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface $O_3$ for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for $O_3$ forecasting was not carried out, the model suggested in this study would be very helpful for the citizens of Korea and Japan, especially during the $O_3$ season from May to June.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.336-350
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• 2013

This paper proposes a mathematical modeling-based approach for assessing disaster effects and selecting suitable mitigation alternatives to provide humanitarian relief (HR) supplies, shelter, rescue services, and long-term services after a disaster event. Mitigation steps, such as arrangement of shelter and providing HR items (food, water, medicine, etc.) are the immediate requirements after a disaster. Since governments and non-governmental organizations (NGOs) providing humanitarian aid need to know the requirements of relief supplies and resources for collecting relief supplies, organizing and initiating mitigation steps, a quick assessment of the requirements is the precondition for effective disaster management. Based on satellite images from weather forecasting channels, an area/dimension of the disaster-affected zones and the extent of the overall damage may often be obtained. The proposed approach then estimates the requirements for HR supplies, supporting resources, and rescue services using the census and other government data. It then determines reliable transportation routes, optimum collection and distribution centers, alternatives for resource support, rescue services, and long-term help needed for the disaster-affected zones. A numerical example illustrates the applicability of the model in disaster mitigation planning.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.225-229
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• 2017

In this paper, various units of industrial disaster safety threats as well as local and national facilities unit real-time detection and prevention refer to the corresponding system goes into disaster management preparedness, prevention, response recovery of phase I systematic ICT skills that can be managed more efficiently. In addition, the immediate disaster prevention and preparedness for early forecasting preemptive damage scale and high-tech information exchange technology to overcome the limitations of a human disaster in the field against the analysis and strategy of preemptive disaster safety management with smart risk management and prevention in response and recovery and the scene quickly and efficient mutual cooperation and effective collaboration and cooperation of the Community Center social security presented a smart disaster safety management system.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10a
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• pp.147-158
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• 2001

In general, two methods have been used to predict settlement of soft ground. One method is Terzaghi's one dimensional consolidation theory which gives time-settlement relationship using the standard consolidation test results. The other is forecasting method of ground settlement to be occured in the future using in-situ monitoring data. The above both methods have some defects in application manner or in itself especially in very deep and soft clayey ground. In view of the lessons and experiences of soft ground improvement projects, several techniques were proposed for more accurate theorectical calculation of consolidation settlement as follows ; ① Subdivision of soft ground, ② Consideration of secondary compression, ③ Using the modified compression index, etc. And also, revised hyperbolic fitting method was suggested to minimize the error of predicted future settlement. In addition, revised De-Beer equation of immediate settlement of loose sandy soil was proposed to overcome the tendency to show too small settlement calculation results by original De-Deer equation. And also, considering the various effects of settlement delay in the improved ground by vertical drains, time-settlement caculation equation(Onoue method) was revised to match the tendency of settlement delay by using the characteristics of discharge capacity decreases of vertical drain with time elapse by the pattern of hyperbolic equation.

• Korean Journal of Agricultural Science
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• v.11 no.1
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• pp.61-67
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• 1984

In an attempt to obtain a basic information to develop methods of an effective chemical control and disease forecasting of panicle blast of rice, effects of conidial number of the causal fungus, Pyricularia oryzae, collected in different periods and the rate of leaf blast occurrence on the occurrence of panicle blast were investigated. Conidial number the fungus collected in 5 days before and after heading date were closely related with panicle blast occurrence. But no relationship was obtained between the occurrence rate of leaf blast and that of panicle blast. Considering the incubation period of the disease, we presume that the most effective application periods of chemicals are 5-10 days and 10-15 days before heading, when immediate effective chemicals and slow effective chemicals are applied, respectively.