• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.37-43
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• 2001

Crop status monitoring and yield prediction at higher spatial resolution is a valuable tool in various decision making processes including agricultural policy making by the national and local governments. A prototype crop forecasting system was developed to project the size of rice crop across geographic areas nationwide, based on daily weather pattern. The system consists of crop models and the input data for 1,455 cultivation zone units (the smallest administrative unit of local government in South Korea called "Myun") making up the coterminous South Korea. CERES-rice, a rice crop growth simulation model, was tuned to have genetic characteristics pertinent to domestic cultivars. Daily maximum/minimum temperature, solar radiation, and precipitation surface on 1km by 1km grid spacing were prepared by a spatial interpolation of 63 point observations from the Korea Meteorological Administration network. Spatial mean weather data were derived for each Myun and transformed to the model input format. Soil characteristics and management information at each Myun were available from the Rural Development Administration. The system was applied to the forecasting of national rice production for the recent 3 years (1997 to 1999). The model was run with the past weather data as of September 15 each year, which is about a month earlier than the actual harvest date. Simulated yields of 1,455 Myuns were grouped into 162 counties by acreage-weighted summation to enable the validation, since the official production statistics from the Ministry of Agriculture and Forestry is on the county basis. Forecast yields were less sensitive to the changes in annual climate than the reported yields and there was a relatively weak correlation between the forecast and the reported yields. However, the projected size of rice crop at each county, which was obtained by multiplication of the mean yield with the acreage, was close to the reported production with the $r^2$ values higher than 0.97 in all three years.

• Journal of Climate Change Research
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• v.7 no.2
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• pp.193-203
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• 2016

The importance of forecasting agricultural production is receiving attention while climate change is accelerating. This study suggested three types of crop yield forecasting model for major vegetable crops by using downscaled meteorological information of main production area on farmland level, which identified as limitation from previous studies. First, this study conducted correlation analysis with seven types of farm level downscaled meteorological informations and reported crop yield of main production area. After, we selected three types of meteorological factors which showed the highest relation with each crop species and regions. Parameters were deducted from meterological factor with high correlation but crop species number was neglected. After, crop yield of each crops was estimated by using the three suggested types of models. Chinese cabbage showed high accuracy in overall, while the accuracy of daikon and onion was quiet revised by neglecting the outlier. Chili and garlic showed differences by region, but Kyungbuk chili and Chungnam, Kyungsang garlic appeared significant accuracy. We also selected key meteorological factor of each crops which has the highest relation with crop yield. If the factor had significant relation with the quantity, it explains better about the variations of key meteorological factor. This study will contribute to establishing the methodology of future studies by estimating the crop yield of different species by using farmland meterological information and relatively simplify multiple linear regression models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.167-177
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• 2010

Recent competitive and technological changes during the past decade have accelerated the need for better capital recovery methods. Competition and technology have together shortened the expected lives of property which could not have been forecasted several years ago. Since the usage of technological growth models has been prevalent in various technological forecasting environments, the various forms of growth models have become numerous. Of six such models studied, some models do significantly better than others, especially at low penetration levels in predicting future levels of growth. A set of criteria for choosing an appropriate model for technological growth models was developed. Two major characteristics of an S-shaped curve were elected which differentiate the various models; they are the skewness of the curve and underlying assumptions regarding the variance of error structure of the model.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.3-11
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• 2005

This paper proposes the method for the man power forecasting and performs mid-term(1994-1998) forecasting of telematics man power demands in Korea. Telematics technology has been selected as '839 New IT Growth Engine' by Ministry of Information and Communication (MIC) of Korean Government to boost Korean IT industry for the next 10 years. In order to meet the man power requirement in this telematics industry, accurate forecasting of the man power demand is necessary. The procedures for the forecasting includes study of man power forecasting models, deriving market size of the telematics industry, perform labor productivity analysis, derive the man power structure by the types of the work forces by the types of telematics industry, and finally derive annual man power demands by the worker types and the telematics industry types.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.4
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• pp.363-368
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• 1993

Short-term forecasting of fish catch is of practical importance in fisheries management. Ecosystem models and multi-species models as well as traditional single-species models fall short of predicting power needed for practical management of fisheries resources due to the lack of sufficient data or information for the required parameters. Univariate time series analysis, on the other hand, extracts the information on the stochastic variability from the time series itself and makes estimates of the future stochastic variability. Therefore, it can be used for short-term forecasting with minimum data requirements. ARIMA time series modeling has been applied to the monthly Korean catches of hairtail (Trichiurus lepturus) for $1971{\sim}1988$. Forecasts of hairtail catch were made and compared with the actual catch data from $1989{\sim}1990$ which were not included in the parameter estimation. The results showed a good agreement (r=0.938) between the forecasts and the actual catches with a mean rotative error of $59.5\%$

• Journal of Korea Port Economic Association
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• v.29 no.1
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• pp.67-76
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• 2013

It has been more than four years since the outbreak of global financial crisis. However, the world economy continues to be challenged with new crisis such as the European debt crisis and the fiscal cliff issue of the U.S. The global economic environment remains fragile and prone to further disappointment, although the balance of risks is now less skewed to the downside than it has been in recent years. It's no wonder that maritime business will be bearish since the global business affects the maritime business directly as well as indirectly. This paper, hence, aims to predict the Baltic Dry Index representing the shipping business using the ARIMA-type models and Hodrick-Prescott filtering technique. The monthly data cover the period January 2000 through January 2013. The out-of-sample forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. These forecasting performances are also compared with those of the random walk model. This study shows that the ARIMA models including Intervention-ARIMA have lower rmse than random walk model. This means that it's appropriate to forecast BDI using the ARIMA models. This paper predicts that the shipping market will be more bearish in 2013 than the year 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• Journal of Environmental Science International
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• v.15 no.2
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• pp.141-155
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• 2006

This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval$(T_m)$ was smaller than the sampling time interval$(T_s)$. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient$(R^2)$ 0.7.