• Atmosphere
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• v.27 no.3
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• pp.261-276
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• 2017

The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.

• Journal of Agricultural Extension & Community Development
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• v.19 no.2
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• Journal of Navigation and Port Research
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• v.28 no.4
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• pp.311-315
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• 2004

This paper is aimed to develop a mathematical model for making the forecast information of typhoon's movement such as the estimated movement direction and positions after 24 and 48 hours. The proposed model calculates such kind of information of a typhoon by similar past typhoon's track data which are selected with three similarity criteria among the database of typhoons' tracks for past fifty years. We carried out a simulation forecast with No.14 typhoon formed in 1997, and found that the results of the proposed model were reasonable and it would be suitable for a simulation system for training mariners so that they can take suitable actions to evade the typhoons.

• Journal of the Korean Statistical Society
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• v.7 no.1
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• pp.11-16
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• 1978

Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.47-53
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• 2005

The efficiency of fabrication (fab) operation is one of the key factors in order for a semiconductor manufacturing company to stay competitive. Optimization of manpower and forecasting manpower needs in a modern fab is an essential part of the future strategic planing and a very important to the operational efficiency. As the semiconductor manufacturing technology has entered the 8-inch wafer era, the complexity of fab operation increases with the increase of wafer size. The wafer handling method has evolved from manual mode in 6-inch wafer fab to semi-automated or fully automated factory in 8-inch and 12-inch wafer fab. The distribution of manpower requirement in each specialty varied as the trend of fab operation goes for downsizing manpower with automation and outsourcing maintenance work. This paper is to study the specialty distribution of manpower from the requirement in a typical 6-inch, 8-inch to 12-inch wafer fab. The human resource planning in today’s fab operation shall consider many factors, which include the stability of technical talents. This empirical study mainly focuses on the human resource planning, the manpower distribution of specialty structure and the forecast model of internal demand/supply in current semiconductor manufacturing company. Considering the market fluctuation with the demand of varied products and the advance in process technology, the study is to design a headcount forecast model based on current manpower planning for direct labour (DL) and indirect labour (IDL) in Taiwan’s fab. The model can be used to forecast the future manpower requirement on each specialty for the strategic planning of human resource to serve the development of the industry.

• Child Health Nursing Research
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• v.7 no.4
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• pp.483-493
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• 2001

This study is attempted to define risk factor of youth runaway impulse and to structure forecast model through an extensive analysis of the factors influencing the runaway impulse of youth. The subjects were 610 high school students in Seoul and Kyunggido. The collected data was analysed by SAS. The differences between the runaway impulse group and the non-runaway impulse group were subject to chi-square and t-test. Also logistic regression analysis was conducted on the basis of purposeful selection method for constructing the forecast model. The findings are as follows : the major predicting factors of youth runaway impulse are sex(odds ratio=1.886, p=.009), existence of friends of the opposit sex(odds ratio=2.011, p=.007), anti-social personality(odds ratio= 4.953, p=.000), depressive trend(odds ratio= 2.695, p=.000), family structure(odds ratio= 5.381, p=.000), marital relationship(odds ratio =1.893, p=.009) and also between parents and youth(odds ratio=3.877, p=.000), emotional abuse(odds ratio=1.963, p=.003), authoritative controlled rearing(odds ratio=2.135, p=.005) and stress from school(odds ratio=1.924, p=.008). Therefore, the forecast model will be contribute to the nursing intervention for prevention of runaway youth.

• Atmosphere
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• v.22 no.3
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• pp.287-298
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• 2012

The predictability of heavy precipitation over the Korean Peninsula is studied using THORPEX Interactive Grand Global Ensemble (TIGGE) data. The performance of the six ensemble models is compared through the inconsistency (or jumpiness) and Root Mean Square Error (RMSE) for MSLP, T850 and H500. Grand Ensemble (GE) of the three best ensemble models (ECMWF, UKMO and CMA) with equal weight and without bias correction is consisted. The jumpiness calculated in this study indicates that the GE is more consistent than each single ensemble model. Brier Score (BS) of precipitation also shows that the GE outperforms. The GE is used for a case study of a heavy rainfall event in Korean Peninsula on 9 July 2009. The probability forecast of precipitation using 90 members of the GE and the percentage of 90 members exceeding 90 percentile in climatological Probability Density Function (PDF) of observed precipitation are calculated. As the GE is excellent in possibility of potential detection of heavy rainfall, GE is more skillful than the single ensemble model and can lead to a heavy rainfall warning in medium-range. If the performance of each single ensemble model is also improved, GE can provide better performance.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Journal of Environmental Science International
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• v.25 no.11
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• pp.1521-1530
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• 2016

As a result of dividing typhoon that affected Korean Peninsular between 1999 and 2012 into 7 types of path and entering forecast field and analysis field of RDAPS, until 36 hours from the time of forecast, it is reliable to use the forecast field of RDAPS to predict typhoon and for each typhoon path, the difference between the forecast and the analysis shows normal distribution, which is usable for weather forecast until the $36^{th}$ hour. In the $48^{th}$ hour from the time of forecast, the difference of result depending on each typhoon path increased, which was analyzed to be due to errors in the forecast. It was expected that relatively reasonable results should be shown if the $36^{th}$ hour forecast is used to predict the strength and distribution of strong wind. As a result of using Korean RAM and observing the difference of the maximum damage, reliability was secured up to 36 hours and after 48hours, it was expected that the fluctuation of results may become more severe.