• Proceedings of the KSR Conference
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• 2003.10b
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• pp.35-39
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• 2003

Effort has been given to improve demand forecast methodology of rail system since it can have great impact on project evaluation of rail system investment. However most of demand forecast softwares developed in western countries where concerns have been provided mostly to private transport and they should be updated in order to reflect our country's situation accurately. Therefore, this paper aims, especially focusing on rail system, to do comparison analysis of oversea's passenger demand forecast softwares and provide some ideas to develop the updated demand forecast system which enables to reflect our country's situation accurately. Main conclusions are that we will need to have well described model for real situation. So we will have to study for these aspects for travel demand forecasting system and develop the package architecture.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.111-123
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• 2004

Recently, though damage caused by intensive rainfall and typhoon happens frequently, we could not forecast or predict a disaster, due to the difficulty of obtaining exact information about it. For efficient disaster management, the most urgent need is the preparation of a flood forecast-warning system. Therefore, we need to provide a program that has the ability of inundation analysis and flood forecast-warning using a geographic information system, and using domestic technology rather than that from foreign countries. In this research, we constructed a FDMS(Flood Disaster Management System) that is able to analyze real-time inundation data, and usins the GIS(Ceographic Information System) with prompt analyzing of hydrologic-topographical parameters and runoff-computation. Moreover, by expressing inundation analysis in three-dimensions, we were able to get to the inundation area with ease. Finally, we expect that the application of this method in the (food forecast-warning system will have great role in reducing casualties and damage.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.37-37
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• 2023

There is currently a high-accuracy modern forecasting method that uses machine learning algorithms or artificial neural network models to forecast river water levels or flowrate. As a result, this study aims to develop a mathematical model based on artificial neural networks to effectively forecast river water levels upstream of Tranh Culvert in North Vietnam's Bac Hung Hai irrigation system. The mathematical model was thoroughly studied and evaluated by using hydrological data from six gauge stations over a period of twenty-two years between 2000 and 2022. Furthermore, the results of the developed model were also compared to those of the long-short-term memory neural networks model. This study performs four predictions, with a forecast time ranging from 6 to 24 hours and a time step of 6 hours. To validate and test the model's performance, the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error, and root mean squared error were calculated. During the testing phase, the NSE of the model varies from 0.981 to 0.879, corresponding to forecast cases from one to four time steps ahead. The forecast results from the model are very reasonable, indicating that the model performed excellently. Therefore, the proposed model can be used to forecast water levels in North Vietnam's irrigation system or rivers impacted by tides.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.8
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• Ocean and Polar Research
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• v.40 no.3
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• pp.99-114
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• 2018

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about $0.2m\;s^{-1}$ is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

• Journal of Korea Water Resources Association
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• v.45 no.10
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• pp.997-1008
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• 2012

To construct the practical forecast and warning system for distant tsunamis suitable for Korea the state-of-art of the forecast and warning systems of United States and Korea is investigated, and the action conducted by Korea for the case of 2011 East Japan Tsunami is also analysed. The tsunami sources and propagation characteristics of distant tsunamis that affect the Korean coastal area are considered along with the capability of earthquake monitering and numerical simulation and the available experts to propose the effective forecast and warning system for distant tsunamis.

• Journal of Sensor Science and Technology
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• v.8 no.3
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• pp.253-258
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• 1999

This paper is written for the purpose of obtaining the information about the weather easily by the development of weather forecast system sensing temperature, humidity, and atmospheric pressure as key information. For this, data is obtained from the Weather Bureau, and analyzed in order to set a standard of weather forecast from the collected data. The pressure sensor and temperature-humidity sensor are fabricated using the piezoresistive effect of semiconductor, which are used to collect data. The weather forecast system is made using microprocessor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.