The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

본 연구는 연안해양 수치모델에 활용되는 LDAPS 강우예보 자료의 시공간적 오차와 한계점을 분석하고 자료의 신뢰성을 검증 하였다. LDAPS 강우자료의 검증은 진해만 주변 우량계 3개소를 기준으로 2020년의 강우를 비교하였으며 우량계와 LDAPS의 비교 결과, LDAPS 강우자료는 장기적인 강우의 경향은 대체로 잘 재현하였으나 단기적으로는 큰 차이를 보였다. 정량적인 강우량 오차는 연간 197.5mm였으며, 특히 하계는 285.4mm로 나타나 계절적으로 강우변동이 큰 시기일수록 누적 강우량의 차이가 증가하였다. 강우 발생 시점 의 경우 약 8시간의 시간 지연을 나타내어 LDPAS 강우자료의 시간적 오차가 연안해양환경 예측 시 정확도를 크게 감소시킬 수 있는 것 으로 나타났다. 연안의 강우를 정확히 반영하지 못하는 LDAPS 강우자료를 무분별하게 사용할 경우 연안역에서 오염물질 확산 또는 극한 강우로 인한 연안환경 변화 예측에 심각한 문제를 발생시킬 수 있으며 LDAPS 강우자료의 적절한 활용을 위해서는 검증과 추가적인 개선 을 통한 정확도 향상이 필요하다.

최근 국민 삶의질 향상, 여가 활동 다변화, 인구구조의 변화 등으로 관광수요 증가 및 관광활동이 다양화되고 있다. 특히 연안 도시의 경우, 육상 관광 요소와 해양관광 요소가 공존하는 지역으로 다양한 요인이 관광수요에 영향을 미치고 있다. 본 연구 목적은 본 연구는 행위자 기반의 데이터를 활용하여 관광규모의 시계열 분석을 통해 예측 정확도를 향상시키고, 영향요인을 탐색하고자 한다. 연구 대상은 부산 지역 내 기초자치단체이며, 데이터는 월단위의 관광객수와 관광소비금액을 활용하였다. 연구방법으로 확정적(결정적) 모형 인 단변량 시계열 분석과 영향요인을 파악하기 위해 SARIMAX 분석을 수행하였다. 영향요인은 관광소비성향을 설정하였으며, 업종별 소 비금액과 SNS 언급량을 중심으로 설정하였다. 연구결과 COVID-19를 고려하지 않은 시계열 모형과 고려한 모형 간의 정확도(RMSE 기준) 차이가 지역별로 최소 1.8배에서 최대 32.7배 향상되었다. 또한 영향요인을 보면 관광소비업종과 SNS 트렌드가 관광객수와 관광소비금액 에 유의한 영향을 미치고 있다. 따라서 미래 수요예측을 위해서는 외적 영향을 고려하고, 관광객의 소비성향과 관심도가 지역관광 측면에 서 고려 대상이 된다. 본 연구는 연안도시인 부산 지역의 미래 관광수요 예측과 관광규모에 미치고 있는 영향요인을 파악하여 정부 관광 정책 및 관광추세를 고려한 관광수요태세 마련을 위한 정책 의사결정에 기여하고자 한다.

PURPOSES : This study analyzes the estimated traffic volumes on roads and railways based on econometrics. METHODS : The accuracy of traffic forecasting was analyzed based on the average difference between predicted and actual values. This study distinguishes itself from existing literature by conducting a comparative analysis categorized by project type. In this study, econometric analyses, including bias and efficiency evaluation, were conducted for 308 projects in Korea. RESULTS : We conducted econometric analysis by dividing the data into project types. This study examines the accuracy of estimates in South Korea's road and railway projects concerning various factors, including project types (mobility-focused or accessibility-focused), implementing agencies, and the performance of preliminary feasibility studies. Notably, it identifies a tendency for overestimation, particularly in railway projects and mobility-focused road projects, such as expressways and national highways, as well as in projects executed by local governments. The mean percentage error (MPE) for the analyzed projects was -46.62%, indicating a significant overestimation bias with resulting inefficiencies. However, our analysis revealed that road projects, particularly those accompanied by preliminary feasibility studies and implemented by the central government, exhibited reduced bias and improved efficiency. The presence or absence of preliminary feasibility studies significantly influenced estimation bias. Interestingly, even when preliminary feasibility studies are conducted, the choice of the implementing agency remains a crucial factor affecting estimation bias. In addition, railway projects continue to demonstrate a notable overestimation bias, warranting further attention. CONCLUSIONS : Considering bias, efficiency, and MPE is advisable when forecasting traffic.

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

Most demand forecasting studies for telecommunication services have focused on estimating market size at the introductory stage of new products or services, or on suggesting improvement methods of forecasting models. Although such studies forecast business growth and market sizes through demand forecasting for new technologies and overall demands in markets, they have not suggested more specific information like relative market share, customers’ preferences on technologies or service, and potential sales power. This study focuses on the telecommunication service industry and explores ways to calculate the relative market shares between competitors, considering competitive situations at the introductory stage of a new mobile telecommunication service provider. To reflect the competitive characteristics of the telecommunication markets, suggested is an extended conjoint analysis using service coverage and service switching rates as modification variables. This study is considered to be able to provide strategic implications to businesses offering existing service and ones planning to launch new services. The result of analysis shows that the new service provider has the greatest market share at the competitive situation where the new service covers the whole country, offers about 50% of existing service price, and allows all cellphones except a few while the existing service carrier maintains its price and service and has no response to the new service introduction. This means that the market share of the new service provider soars when it is highly competitive with fast network speed and low price.

The impact of vertical grid-nesting on the tropical cyclone intensity and track forecast was investigated using the Weather Research and Forecast (WRF) version 3.8 and the initialization method of the Structure Adjustable Balanced Bogus Vortex (SABV). For a better resolution in the central part of the numerical domain, where the tropical cyclone of interest is located, a horizontal and vertical nesting technique was employed. Simulations of the tropical cyclone Sanba (16th in 2012) indicated that the vertical nesting had a weak impact on the cyclone intensity and little impact on the track forecast. Further experiments revealed that the performance of forecast was quite sensitive to the horizontal resolution, which is in agreement with previous studies. The improvement is due to the fact that horizontal resolution can improve forecasts not only on the tropical cyclone-scale but also for large-scale disturbances.

We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2–6.3%.

This paper proposed data driven techniques to forecast the time point of water management of the water reservoir without measuring manganese concentration with the empirical data as Juam Dam of years of 2015 and 2016. When the manganese concentration near the surface of water goes over the criteria of 0.3mg/l, the water management should be taken. But, it is economically inefficient to measure manganese concentration frequently and regularly. The water turnover by the difference of water temperature make manganese on the floor of water reservoir rise up to surface and increase the manganese concentration near the surface. Manganese concentration and water temperature from the surface to depth of 20m by 5m have been time plotted and exploratory analyzed to show that the water turnover could be used instead of measuring manganese concentration to know the time point of water management. Two models for forecasting the time point of water turnover were proposed and compared as follow: The regression model of CR20, the consistency ratio of water temperature, between the surface and the depth of 20m on the lagged variables of CR20 and the first lag variable of max temperature. And, the Box-Jenkins model of CR20 as ARIMA (2, 1, 2).

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.