• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.172-172
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• 2022

분산계수는 하천에서 오염물질의 혼합능을 파악할 수 있는 대표적인 인자이다. 특히 하수처리장 방류수 혼합예측과 같이 횡 방향 혼합에 대한 예측이 중요한 경우, 하천의 지형적, 수리학적 특성을 고려한 2차원 횡 분산계수의 결정이 필요하다. 2차원 횡 분산계수의 결정을 위해 기존 연구에서는 추적자실험결과로부터 경험식을 만들어 횡 분산계수 산정에 사용해왔다. 회귀분석을 통한 경험식 산정을 위해서는 충분한 데이터가 필요하지만, 2차원 추적자 실험 건수가 충분치 않아 신뢰성 높은 경험식 산정이 어려운 상황이다. 따라서 본 연구에서는 SMOTE기법을 이용하여 횡분산계수 실험데이터를 증폭시켜 이로부터 횡 분산계수 경험식을 산정하고자 한다. 또한 다중선형회귀분석을 통해 도출된 경험식의 한계를 보완하기 위해 다양한 머신러닝 기법을 적용하고, 횡 분산계수 산정에 적합한 머신러닝 기법을 제안하고자 한다. 기존 추적자실험 데이터로부터 하폭 대 수심비, 유속 대 마찰유속비, 횡 분산계수 데이터 셋을 수집하였으며, SMOTE 알고리즘의 적용을 통해 회귀분석과 머신러닝 기법 적용에 필요한 데이터그룹을 생성했다. 새롭게 생성된 데이터 셋을 포함하여 다중선형회귀분석을 통해 횡 분산계수 경험식을 결정하였으며, 새로 제안한 경험식과 기존 경험식에 대한 정확도를 비교했다. 또한 다중선형회귀분석을 통해 결정된 경험식은 횡 분산계수 예측범위에 한계를 보였기 때문에 머신러닝기법을 적용하여 다중선형회귀분석에 대한 예측성능을 평가했다. 이를 위해 머신러닝 기법으로서 서포트 벡터 머신 회귀(SVR), K근접이웃 회귀(KNN-R), 랜덤 포레스트 회귀(RFR)를 활용했다. 세 가지 머신러닝 기법을 통해 도출된 횡 분산계수와 경험식으로부터 결정된 횡 분산계수를 비교하여 예측 성능을 비교했다. 이를 통해 제한된 실험데이터 셋으로부터 2차원 횡 분산계수 산정을 위한 데이터 전처리 기법 및 횡 분산계수 산정에 적합한 머신러닝 절차와 최적 학습기법을 도출했다.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.159-164
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• 2024

The domestic petroleum market is highly sensitive to changes in international oil prices. So, it is important to identify and respond to those changes. In particular, it is necessary to clearly understand the factors causing the price fluctuations of gasoline, which exhibits high consumption. International gasoline prices are influenced by global factors such as gasoline supplies, geopolitical events, and fluctuations in the U.S. dollar. However, previous studies have only focused on gasoline supplies. In this study, we explore the causal relationship between economic indicators and international gasoline prices using various machine learning-based regression models. First, we collect data on various global economic indicators. Second, we perform data preprocessing. Third, we model using Multiple linear regression, Ridge regression, and Lasso(Least Absolute Shrinkage and Selection Operator) regression. The multiple linear regression model showed the highest accuracy at 96.73% in test sets. As a result, Our Multiple linear regression model showed the highest accuracy at 96.73% in test sets. We will expect that our proposed model will be helpful for domestic economic stability and energy policy decisions.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Proceedings of the Korea Water Resources Association Conference
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• 1996.05a
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• pp.211-216
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• 1996

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• 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.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.113-126
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• 2008

This research aims to explore the effects of multicollinearity on the reliability and goodness of fit of logit model. To investigate the effects of multicollinearity on the multinominal logit model, numerical experiments are performed. The exploratory variables(attributes of utility functions) which have a certain degree of correlations from (rho=) 0.0 to (rho=) 0.9 are generated and rho-squares and t-statistics which are the indices of goodness of fit and reliability of logit model are traced. From the well designed numerical experiments, following findings are validated : 1) When a new exploratory variable is added, some of rho-squares increase while the others decrease. 2) The higher relations between generic variables lead a logit model worse with respect to goodness of fit. 3) Multicollinearity has a tendency to produce over-evaluated parameters. 4) The reliability of the estimated parameter has a tendency to decrease when the correlations between attributes are high. These results suggest that we have to examine the existence of multicollinearity and perform the proper treatments to diminish multicollinearity when we develop logit model.