• Proceedings of the Korea Water Resources Association Conference
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• 1991.07a
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• pp.119-132
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• 1991

장기 수자원 개발계획의 수립에 필요한 월유출량의 추정을 위해, 수위계획지점의 유출자료를 사용하여 다중회귀분석으로 회귀모형을 수립함으로써 미계측지점의 월유출량 추정을 가능토록 하였다. 사용한 자료는 총 48개 수위관측소의 월유출량 및 기상·지상인자이며 이중 43개지점은 모형의 개발에 나머지 5개 지점은 모형의 검증에 이용하였다. 또한 모형을 유역별모형과 전체모형, 평균치모형과 개별자료모형으로 구분하여 모형-1, 모형-2, 모형-3 그리고 모형-4의 4개 모형을 수립하였으며, 검증결과 모형-2가 가장 적절한 모형으로 판단 되었다. 선정된 회기모형과 기존의 가지야마공식의 적용성을 통계적 방법에 의해 비교한 결과, 본 다중회기모형의 연유출량 뿐아니라 월별유출량의 변화성향을 매우 잘 나타내고 있으며, 적용 또한 용이함이 입증되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.73-87
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• 2016

The purpose of this study is to investigate the empirical results of Analytic Hierarchy Process/Data Envelopment Analysis-Assurance Region(AHP/DEA-AR) by using multiple regression analysis during the period of 2009-2012 with 5 inputs (number of gantry cranes, number of berth, berth length, terminal yard, and mean depth) and 2 outputs (container TEU, and number of direct calling shipping companies). Assurance Region(AR) is the most important tool to measure the efficiency of seaports, because individual seaports are characterized in terms of inputs and outputs. Traditional AHP and multiple regression analysis techniques have been used for measuring the AR. However, few previous studies exist in the field of seaport efficiency measurement. The main empirical results of this study are as follows. First, the efficiency ranking comparison between the two models (AHP/DEA-AR and multiple regression) using the Wilcoxon signed-rank test and Mann-Whitney signed-rank sum test were matched with the average level of 84.5 % and 96.3% respectively. When data for four years are used, the ratios of the significant probability are decreased to 61.4% and 92.5%. The policy implication of this study is that the policy planners of Korean port should introduce AHP/DEA-AR and multiple regression analysis when they measure the seaport efficiency and consider the port investment for enhancing the efficiency of inputs and outputs. The next study will deal with the subjects introducing the Fuzzy method, non-radial DEA, and the mixed analysis between AHP/DEA-AR and multiple regression analysis.

• Journal of Korea Water Resources Association
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• v.42 no.5
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• pp.375-383
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• 2009

This study examined hydraulic characteristics on diagonal weirs with hydraulic experiment and presented a discharge coefficient equation utilizing multiple regression analysis for various design conditions. This study had a object in designing efficiently diagonal weirs utilizing this equation. Diagonal weirs maintained uniformly upstream water level than rectangular suppressed weirs. Also, as installation degrees of diagonal weirs increased, diagonal weirs increased maintenance effects of a upstream water level. Because of these characteristics, diagonal weirs were suitable to canal system. This study presented discharge coefficient equations for diagonal weirs utilizing simple regression analysis. But, these equations are some restrictions on degrees. Therefore, this study presented an equation to estimate directly discharge coefficients to various degrees utilizing multiple regression analysis. This equation was verified by making use of analyses of $R^2$, the sum of residuals, MAPE. Therefore, this equation is enable to make good use of a design in diagonal weirs.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.57-64
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• 2009

This study intends to evaluate of the characteristics of pavement deformation and develop the model for prediction model in the asphalt layer using a regression analysis. In test, there are two different asphalt binders and 5 different aggregate types. The air voids of hot mix asphalt are 6% and 10% for target value. Repeated triaxial compression test with 3 different confining pressures was used for test at 3 different test temperatures. It is going to verify the main parameters for permanent deformation of HMA and to develop the distress model. This paper is to figure out the factor affecting the pavement deformation, and then to develop model the pavement deformation for asphalt mixture. Also, the reliability of prediction model has been studied. The permanent deformation prediction model for asphalt mixtures with temperature, loading time, and air voids has been developed and the proposed permanent deformation prediction model has been validated by using the multiple regression approach which is called Statistical Package for the Social Sciences(SPSS).

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.9-19
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• 2024

As using public monitoring data, analysing a trends of water quality change, establishing a criteria to determine abnormal status and constructing a regression model that can predict Chlorophyll-a, an indicator of eutrophication, was studied. Accordingly, the three freshwater lakes were selected, approximately 20 years of water quality monitoring data were analyzed for periodic changes in water quality each year using regression analysis, and a method for determining abnormalities was presented by the standard deviation at confidence level 95%. By calculating the temporal change rate of Chlorophyll-a from irregular observed data, analyzing correlations between the rate and other water quality items, and constructing regression models, a method to predict changes in Chlorophyll-a was presented. The results of this study are expected to contribute to freshwater lake water quality management as an approximate water quality prediction method using the statistical model.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.53-77
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• 1994

In a certain stochastic process, Cox's regression model is used to analyze multistate survival data. From this model, the regression parameter vectors, survival functions, and the probability of being in response function are estimated based on multistate Cox's partial likelihood and nonparametric likelihood methods. The asymptotic properties of these estimators are described informally through the counting process approach. An example is given to likelihood the results in this paper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.578-580
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• 2019

우리 나라의 딸기 수경재배 면적은 2002년 5ha로 시작해서, 2007년에는 84ha, 2012년에는 317ha, 2017년에 1,575ha로 매년 30% 이상 급속하게 성장하고 있다. 이런 경향은 수경재배가 토양재배보다 작업이 용이하여 노동시간이 절약되며, 수량을 더 많이 생산할 수 있기 때문이다. 하지만, 공급양액을 배액으로 흘려버리는 비순환식 수경재배 방식이 증가 하면서 환경오염을 유발시킬 뿐만 아니라 수경재배 운영비용의 증가를 가져오고 있다. 본 논문은 작물 생장에 최적화된 양액공급을 위해 상관관계 분석 및 다중 선형 회귀 모델 기반의 딸기 수경재배 환경에서의 최적 양액 흡수량을 분석하고 추정해 보았다. 분석 결과, 수경재배 환경정보(일사량, 온도, 습도, CO₂ 등)를 대상으로 일사량 및 온도가 습도 및 CO₂에 비해 딸기재배를 위한 양액 흡수량에 더 큰 영향을 주는 것으로 분석되었고, 다중 선형 회귀 모델을 통한 회귀식의 R-Square값은 0.358으로 나타났다.