• Journal of Energy Engineering
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• v.18 no.2
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• pp.114-124
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• 2009

The Ministry of Knowledge and Economy (MKE) establishes the Basic Plan for Long-Term Electricity Supply and Demand(BPE) biannually, a governmental plan for the stable electricity supply. This study investigated the effects of the electric demand forecast on the energy mix. A simplified simulation model was developed, which replaces the WASP program developed by the KPX and verified by comparing both results. Three different electric demand scenarios were devised based upon the 2005 electric demand forecast: Proper, 5 % higher, and 5% lower. The simplified model calculates the energy mix for each scenario of the year 2005. Then it calculates the energy mix for the proper electric demand forecast of the year 2007 using the energy mixes of the three scenarios as the initial conditions, so that it reveals the effect of electric demand forecast of the previous BPE on the energy mix of the next BPE. As the proper electric demand forecasts of the year 2005 and 2007 are the same, there is no change in the previous and the next BPEs. However when the electric demand forecasts were 5% higher in the previous BPE and proper in the next BPE, some of the planned power plant construction in the previous BPE had to be canceled. Similarly, when the electric demand forecasts were 5% lower in the previous BPE and proper in the next BPE, power plant construction should be urgently increased to meet the increased electric demand. As expected the LNG power plants were affected as their construction periods are shorter than coal fired or nuclear power plants. This study concludes that the electric demand forecast is very important and that it has the risk of long term energy mix.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• Journal of Korean Academy of Nursing
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• v.34 no.6
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• pp.954-963
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• 2004

Purpose: This study was designed to identify the variables affecting male adolescents' sexual intercourse through a comprehensive analysis of individual and environmental factors. Method: The subjects of this descriptive survey on causal relations were 462 subjects enrolled in liberal and vocational high schools selected on a convenience sampling basis. The data collected from May-July 2002 was put to logistic regression analysis to build a forecast model. Findings: 1) Individual factors such as school record, experience seeking, non-inhibition and sexual permissiveness, 2) family factors such as parental living arrangement, 3) school factors such as career tract and 4) peer factors such as having a boy/girl friend were identified as significant variables forecasting sexual intercourse. Conclusion and Recommendation: The theoretical model built on the basis of the major findings of this study will hopefully help promote a wholesome youth culture related to sexual intercourse. It is recommended that a program be developed that can help control the variables identified in this study along with a follow-up study to verify the model.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• Geomechanics and Engineering
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• v.8 no.1
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• pp.33-52
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• 2015

Based on the Mohr-Coulomb failure criterion, a gradient-dependent plastic model that considers the strain-softening behavior is presented in this study. Both triaxial shear tests on conventional specimen and precut-specimen, which were obtained from an ancient landslide, are performed to plot the post-peak stress-strain entire-process curves. According to the test results of the soil strength, which reduces from peak to residual strength, the Mohr-Coulomb criterion that considers strain-softening under gradient plastic theory is deduced, where strength reduction depends on the hardening parameter and the Laplacian thereof. The validity of the model is evaluated by the simulation of the results of triaxial shear test, and the computed and measured curves are consistent and independent of the adopted mesh. Finally, a progressive failure of the ancient landslide, which was triggered by slide of the toe, is simulated using this model, and the effects of the strain-softening process on the landslide stability are discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Journal of Families and Better Life
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• v.27 no.2
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• pp.203-211
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• 2009

This study was conducted to evaluate a forecast model designed to describe a vocational high school for runaway students. The study included 2000 adolescents from the KEEP(Korean Education and Employment Panel). A Data mining decision tree model revealed that: (1) Suicide ideation was a risk factor for running away among smokers. (2) High self-evaluation was a risk factor for running away among individuals that smoked and had no suicide ideation. (3) Drinking was predicted as a risk factor for no smokers, while family life dissatisfaction was predicted as a risk factor among non-smokers that drank. (4) Negative relationship with mother was predicted as a risk factor among non-drinking non-smoking.

• Journal of Information Technology Applications and Management
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• v.24 no.2
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• pp.81-96
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• 2017

Forecasting long-term mobile service demand is inevitable to establish an effective frequency management policy despite the lack of reliability of forecast results. The statistical forecasting method has limitations in analyzing how the forecasting result changes when the scenario for various drivers such as consumer usage pattern or market structure for mobile communication service is changed. In this study, we propose a dynamic model of the mobile communication service market using system dynamics technique and forecast the future demand for long-term mobile communication subscriber based on the dynamic model, and also experiment on the change pattern of subscriber demand under various scenarios.

• Journal of Astronomy and Space Sciences
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• v.40 no.1
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• pp.11-18
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• 2023

The ionosphere is one of the key components of the near-Earth's space environment and has a practical consequence to the human society as a nearest region of the space environment to the Earth. Therefore, it becomes essential to specify and forecast the state of the ionosphere using both the observations and numerical models. In particular, numerical modeling of the ionosphere is a prerequisite not only for better understanding of the physical processes occurring within the ionosphere but also for the specification and forecast of the space weather. There are several approaches for modeling the ionosphere, including data-based empirical modeling, physics-based theoretical modeling and data assimilation modeling. In this review, these three types of the ionospheric model are briefly introduced with recently available models. And among those approaches, fundamental aspects of the physics-based ionospheric model will be described using the basic equations governing the mid-latitude ionosphere. Then a numerical solution of the equations will be discussed with required boundary conditions.