• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.19-34
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• 2018

The purpose of this study is to estimate monthly agricultural reservoir storage with multiple linear regression model(MLRM) based on reservoir storage and meteorological data. The regression model was developed using 15 years(2002 to 2016) of 3,067 reservoirs by KRC(Korea Rural Community) and 63 meteorological stations by KMA (Korean Meteorological Administration), and the MLRM showed the determination coefficient($R^2$) of 0.51~0.95. The MLRM was applied to 9 selected reservoirs among the whole reservoirs and validated with $R^2$ of 0.44~0.81. The ROC(Receiver Operating Characteristics) analysis of Reservoir Drought Index(RDI) classified by comparing the present reservoir storage with normal year(1976~2005 average) reservoir storage showed average value of 0.64 for 2 years(2015~2016) with the highest value of 0.70 for winter period, lowest value of 0.58 for summer period. If 1 to 3 months weather forecasting data such as Glosea5 produced by KMA are applied, the predicted monthly reservoir storage from the MLRM can be a useful information for agricultural drought pre-preparation.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.475-476
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• 2015

전력수요는 다양한 외부요인으로부터 영향을 받으므로 전력수요 예측 시 각 요인과의 상관관계를 고려할 필요가 있다. 본 논문은 Stepwise 다중회귀분석법을 이용한 일일 최대전력수요 예측 방법을 제시하였다. 사례연구에서는 2014년 평일 전력수요데이터를 이용하여 제안된 예측방법을 적용하고 그 결과를 평가하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.310-312
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• 2000

추정을 위한 방법으로는 K-NN과 회귀분석, 신경망 등의 다양한 방법을 적용할 수 있다. 그러나 K-NN의 경우 거리에 의해서만 결과를 추정하므로 각 속성에 대한 가중치가 속성 값들의 간격에 의해 결정되고, 회귀분석은 하나의 선으로 데이터의 경향을 표현하므로 속성의 가중치는 고려되지만, 데이터의 분포가 넓을 경우에는 많은 오차를 포함하게 되는 데이터에 의존적인 문제가 존재한다. 따라서 본 연구에서는 이러한 방법들을 혼합하여 데이터에 의존적인 문제를 보안할 수 있는 다중분석방법을 제안한다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.351-352
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• 2016

조선소의 생산계획 담당자가 기존 실적 정보를 이용하여 관심 대상인 미래의 생산계획 상황을 보다 쉽게 예측하여 생산계획의 적중률을 높일 수 있도록 할 예정이다. 2006년에서 2016년의 S조선사의 2차 데이터를 이용하여 요인 분석을 하고 다중회귀분석 모형을 설계하여 활용하는 프로세스를 설계한다. 사례 연구를 통해 연구 모형이 적절한지를 검증할 계획이다.

• Water for future
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• v.24 no.3
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• pp.71-82
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• 1991

Methods of predicting water resources availiability of a river basin can be classified as empirical formula, water budget analysis and regression analysis. The purpose of this study is to develop a method to estimate the monthly runoff required for long-term water resources development project. Using the monthly runoff data series at gaging stations alternative multiple regression models were constructed and evaluated. Monthly runoff volume along with the meteorological and physiographic parameters of 48 gaging stations are used, those of 43 stations to construct the model and the remaining 5 stations to verify the model. Regression models are named to be Model-1, Model-2, Model-3 and Model-4 developing on the way of data processing for the multiple regressions. From the verification, Model-2 is found to be the best-fit model. A comparison of the selected regression model with the Kajiyama's formula is made based on the predicted monthly and annual runoff of the 5 watersheds. The result showed that the present model is fairly resonable and convinient to apply in practice.

• Journal of the Korean GEO-environmental Society
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• v.18 no.12
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• pp.65-75
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• 2017

Flowability and strength with curing time of controlled low-strength material (CLSM) are required differently according to the construction purpose. In this paper, the flowability and strength were estimated from the mixing ratio of CLSM using multiple regression analysis to design the CLSM. The flow values and strength at 12 hrs and 7days were measured in accordance with the mixing ratio of CLSM which consists of 7 different materials, such as CSA expansive agent, ordinary Portland cement, fly ash, sand, silt, water, and accelerator. The multiple regression was performed with the proportions of each material of CLSM as independent variables and the measured properties as dependent variables using SPSS Statistics 23 which is a statistical analysis program. The regression coefficients were estimated from the first to third order equation models for the materials. From the results, the third order model for the flow values and the first order models for 12hrs and 7days strength are the most appropriate models. This study suggests that the mixing ratio required for constructions may be effectively estimated from the regression models about the characteristics of CLSM, before performing experimental tests.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.151-163
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• 2018

The objective of this study was to estimate LOADEST (LOAD Estimator) coefficients for simulating pollutant loads in ungauged watersheds. Regression models of LOADEST were used to simulate pollutant loads, and the multiple linear regression (MLR) was used for coefficients estimation on watershed characteristics. The fifth and third model of LOADEST were selected to simulate T-N (Total-Nitrogen) and T-P (Total-Phosphorous) loads, respectively. The results and statistics indicated that regression models based on LOADEST simulated pollutant loads reasonably and model coefficients were reliable. However, the results also indicated that LOADEST underestimated pollutant loads and had a bias. For this reason, simulated loads were corrected the bias by a quantile mapping method in this study. Corrected loads indicated that the bias correction was effective. Using multiple regression analysis, a coefficient estimation methods according to the watershed characteristic were developed. Coefficients which calculated by MLR were used in models. The simulated result and statistics indicated that MLR estimated the model coefficients reasonably. Regression models developed in this study would help simulate pollutant loads for ungauged watersheds and be a screen model for policy decision.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.195-204
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• 2007

In order to represent multiple regression data, an alternative graphical method, called as SSR Plot, is proposed by using geometrical description methods. This plot uses the relation that the sum of sqaures for regression (SSR) of two explanatory variables is known as the sum of the SSR of one variable and the increase in the SSR due to the addition of other variable to the model that already contains a variable. This half circle shaped SSR plot contains vectors corresponding explanatory variables. We might conclude that some explanatory variables corresponding to vectors which locate near the horisontal axis do affect the response variable. Also, for the regression model with two explanatory variables, a magnitude of the angle between two vectors can be identified for suppression.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.69-77
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• 2015

The PMV regression analysis was conducted for this model based on a database of the PMV variables. PMV regression model simplification was completed through sensitivity and data analysis. The simplified PMV regression model's and Fanger PMV model was confirmed through MAE and RMSE. And the EMS in EnergyPlus was used to establish a simplified PMV regression analysis-based thermal comfort control. Also, the thermal comfort controls based on simplified PMV model and the Fanger PMV model were applied to the building model, it was confirmed that both controls met the thermal comfort range in more than 90% of cases during the air conditioning period.

• The Journal of Engineering Geology
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• v.16 no.1 s.47
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• pp.85-96
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• 2006

This study was conducted to suggest a method to determine weighting values of each parameter of the RMR system considered with geologic characteristics of a study area. This study reviewed the weighting values of the RMR system for the Cretaceous sedimentary rocks distributed in Ulsan area. Based on the data of field survey at the study area, a multiple regression analysis was used to set up an optimal weighting values of the RMR parameters. For the multiple regression analysis, each parameter of the RMR and the slope gradient were regarded as the independent variable and the dependent variable, respectively. The analysis result suggested a modified weighting values of the RMR parameters as follows; 30 for the intact strength of rock; 18 for RQD; 8 for spacing of discontinuities; 32 for the condition of discontinuities; and 12 for ground water.