• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.27-27
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• 2017

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Journal of the Korean Professional Engineers Association
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• v.15 no.1
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• pp.46-50
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• 1982

Following rapid industrial development and urbanization in Korea, there is a need for the Government to implement effective control of pollution and to undertake specific schemes in areas where pollution of the environment is severe. In response to this need, Government of Korea prepare Han River Basin Environmental Master Plan Project for water, air, solid waste to cover environmental protection of the Han River Basin. The Project area is approximately 27,000 sq. Km extending over Seoul, Kyunggi, Kwangwon, Chungbuk Province. The total population of Master Plan Project area is approximately 11.6 million, or one-third of the total population of Korea. There are about 8,000 industries, including those located in 16 industrial complexes, in the project area. The scope of work and terms of reference are the following: (1) A Summary of existing land use and forecasts for changes in land use by the year 2,000. (2) Emission inventories for air, waste water, and solid wastes. (3) Forecasts of future population growth patterns and pollution loadings. (4) Identification of specific projects needs to reduce pollution levels and satisfy the environmental quality standards. (5) A Program of enforcement to include (i) self monitoring, and (ii) governmental inspections and surveillance. (6) A program for quality improvement and quality assurance of environmental measurements. (7) Reports summarizing all data collected analyzed during the study. (8) Conceptual design and feasibility studies, including cost estimates, for needed pollution control projects. (9) A financial plan for future detailed design and construction of public facilities, for financial incentives to industry, and for user charges for industrial use of public treatment of disposal works.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.46-78
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• 2000

This paper is to introduce recent collaborative activities in agricultural weather information services among institutions in Korea as well as key concepts for understanding agrometeorological services. KMA and RDA have agreed upon the establishment of the Joint Committee for Agrometeorolgy at national level to strengthen the national agrometeorological services in data collection, information production, research, and services to end-users of agrometeorological information in Korea. Several on-going joint projects in agrometeorology by RDA/KMA are introduced in brief. The projects being developed are : Strengthening of the Joint Committee of agrometeorology, Extension of observation network for agricultural weather, Production of the detailed agrometeorological information based on numerical weather forecasts, Development of seasonal and interannual weather forecasts for agricultural applications, Information network system for supporting agrometeorological research, and Improvement of agrometeorological information services at national and regional level. Strengthening of programs for the education and training of agrometeorologists will be impending responsibilities of the government. The government must consider establishment of organizations dedicated to and in charge of national agrometeorological services to end-users. RDA and KMA should play a major role to obtain this goal, based on a close cooperation with universities, scientific societies, and other relevant institutions. If this plan is successful, major infrastructures and services in agrometeorology shall be established in the next 5 years, and we can contribute to regional and global societies through sharing experiences and know-hows.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Survey Research
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• v.3 no.1
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• pp.91-118
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• 2002

Korean election polls has a history of fifteen years, which may seem too short while those of advanced countries such as the U.S and the UK has a history of one hundred fifty years. However, with various and creative attempts, some of the Korean research companies have developed election polls methods and theories which can be applied to Korean society. They also elevated accuracy of the election forecasts. In spite of short history the rational and scientific polls and forecasts done by some of the research companies including Gallup Korea are commented that they have improved the quality of Korean election culture. In the article, we will look into the process of the election polls which have been done seven times for 15 years, and then deal with the meaning of the election polls.