• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Publications of The Korean Astronomical Society
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• v.13 no.1 s.14
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• pp.39-54
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• 1998

We have developed solar observational system in the department of Astronomy & Space Sciences of KyungHee University, in order to monitor solar activities and construct solar database for space weather forecasting at maximum of 23rd solar cycle, as well as an solar education and exercise for undergraduate students. Our solar observational system consists of the full disk monitoring system and the regional observation system for H a fine structure. Full disk monitoring system is made of an energy rejection filter, 16cm refractor, video CCD camera and monitor. Monitored data are recorded to VHS video tape and analog output of video CCD can be captured as digital images by the computer with video graphic card. Another system for regional observation of the sun is made of energy rejection filter, 21cm Schmidt-Cassegrain reflector, H a filter with 1.6A pass band width and $375\times242$ CCD camera. We can observe H a fine structure in active regions of solar disk and solar limb, by using this system. We have carried out intense solar observations for a test of our system. It is found that Quality of our H a image is as good as that of solar images provided by Space Environmental Center. In this paper, we introduce the basic characteristics of the KyungHee Solar Observation System and result of our solar observations. We hope that our data should be used for space weather forecasting with domestic data of RRL(Radio Research Laboratory) and SOFT(SOlar Flare Telescope).

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.995-1009
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• 2015

Weather radar rainfall data has been recognized for making valuable contributions to short-term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground-based rain gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatio-temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showd a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model.

• Journal of Nutrition and Health
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• v.36 no.9
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• pp.966-973
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• 2003

The purpose of this study was to investigate the utilization level of forecasting methods in contract foodservice management companies. Questionnaires were distributed and collected from 30 foodservice management companies contracted with universities and 49 university foodservices in Seoul and Kyungki area. Statistical data analysis was performed using SPSS/WIN 10.0 based on the production records of Yonsei University foodservices and the weather reports from a meteorological observatory. The results of this study were as follows: 1) The objectives of the fore-casting systems were identified as saving costs through eliminating the leftover, meeting the customer demands, and improving efficiency in food preparation.2) All of the university foodservices were already performing the forecasting methods but in foodservice management companies as a whole,89.7 percents were applying the method and only 55.2 percents had the separate forecasting department. 3) A large number of foodservice staffs in the head office (65.5％) answered that they often utilized intuitive estimates based on the past experiences and records for forecasting while 65.3% managing staffs in the university foodservices answered the same.4) Both in the head office and university foodservices, actual number of meals served were recorded. In the head office, mostly estimated numbers and actual numbers of meals were recorded while estimated, prepared, and actual numbers of meals served were recorded for most of the cases in university foodservices. 5) The primary factors considered for forecasting were the actual production records for the last month, the customer preference for the selected menu items, and the specific day of the week.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.249-249
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• 2023

Spring drought forecasting in South Korea is essential due to the sknewness of rainfall which could lead to water shortage especially in spring when managed without prediction. Therefore, drought forecasting over South Korea was performed in the current study by thoroughly searching appropriate predictors from the lagged global climate variable, mean sea level pressure(MSLP), specifically in winter season for forecasting time lag. The target predictand defined as accumulated spring precipitation(ASP) was driven by the median of 93 weather stations in South Korea. Then, it was found that a number of points of the MSLP data were significantly cross-correlated with the ASP, and the points with high correlation were regionally grouped. The grouped variables with three regions: the Arctic Ocean (R1), South Pacific (R2), and South Africa (R3) were determined. The generalized linear model(GLM) was further applied for skewed marginal distribution in drought prediction. It was shown that the applied GLM presents reasonable performance in forecasting ASP. The results concluded that the presented regionalization of the climate variable, MSLP can be a good alternative in forecasting spring drought.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.617-624
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• 2017

The production of cabbage, which is mainly cultivated in open field, varies greatly depending on weather conditions, and the price fluctuation is largely due to the presence of a substitute crop. Previous studies predicted the production of cabbage using actual weather data, but in this study, we predicted the wholesale price using unstructured agricultural meteorological data on the web. From January 2009 to October 2016, we collected documents including the cabbage on the portal site, and extracted keywords related to weather in the collected documents. We compared the forecast wholesale prices of simple models and unstructured agricultural weather models at the time of shipment. The simple model is AR model using only wholesale price, and the unstructured agricultural weather model is AR model using unstructured agricultural weather data additionally. As a result, the performance of unstructured agricultural weather model was has been found to be more accurate prediction ability.

• Journal of Ocean Engineering and Technology
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• v.22 no.6
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• pp.13-19
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• 2008

Abnormally large swells that appeared on the coast of the East Sea in October in 2005 and 2006 were simulated using SWAN model to examine the accuracy of the model for future forecasting Seawind data calculated based on the weather chart ant bottom topography were used for input data, and the model was operated more than 20 days before the observed swells to avoid the problems from the cold start of the model. The comparisons with observed wind and wave data were unsatisfactory and neededmore improvement in terms of swell component in the wave model as well as the quality of seawind data. The satellite wind and wave data can be good candidates for future comparison of the wave model results in the East Sea.

• Journal of the Korean Society of Visualization
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• v.5 no.2
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• pp.9-13
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• 2007

Typhoon simulation based on dynamical forecasting results is demonstrated by utilizing geodesic model GME (operational global numerical weather prediction model of German Weather Service). It is based on uniform icosahedral-hexagonal grid. The GME gridpoint approach avoids the disadvantages of spectral technique as well as the pole problem in latitude-longitude grids and provides a data structure extremely well suited to high efficiency on distributed memory parallel computers. In this study we made an attempt to simulate typhoon 'NARI' that passed over the Korean Peninsula in 2007. GME has attributes of numerical weather prediction model and its high resolution can provide details on fine scale. High resolution of GME can play key role in the study of severe weather phenomenon such as typhoons. Simulation of future typhoon that is assumed to occur under the global warming situation shows that the life time of that typhoon will last for a longer time and the intensity will be extremely stronger.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.190-194
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• 2018

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.