• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.1
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• pp.49-56
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• 2019

Urban green spaces have been contributing to the improvement of environmental, mental, and physical health for humans. In addition, recent studies showed the potential role of vegetation in reducing the amount of crime in inner-city neighborhoods at the micro-scale level. However, little is known about the positive role of urban green areas in improving urban safety at the regional level. The purpose of this study is to examine the relationship between urban green areas and actual outdoor crime rates, while also considering socio-demographic factors. The study area is the city of Austin, Texas, USA, which consists of 506 block groups. This study utilized socio-demographic factors based on U.S. Census data and vegetation-related factors utilizing GIS and ENVI software. For analyses, the analysis of variance (ANOVA) and an ordinary least square (OLS) regression were utilized. The results from ANOVA showed that yearly crime rates per acre for areas having 0%~25% trees in their neighborhoods were 0.46% and 1.05% higher than those of having 25%~50% and >50% trees in the neighborhoods, respectively. The results from the OLS regression represented that income, NDVI and park rates in neighborhoods were negatively associated with the crime rate per acre, whereas the percentage of minorities and the percentage of teenage school dropouts were positively associated with the crime rate per acre. This study implies that urban green areas may help to improve the safety of urban areas.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.40-53
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• 2021

Purpose: The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0. Results: A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model. Conclusion: Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.29-34
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• 2003

Dispersion pattern for adult citrus red mite (CRM), Panonychus citri (McGregor) using by Taylor's power law (TPL) and Iwao's patchiness regression (IPR) was determined to develop a monitoring method on citrus orchards, on Jeju, in Autumn season, during 2001 and 2002.CRM population was sampled by collecting leaves and fruits. The relationships of CRM adults between leaf and fruit were analyzed by different season. The regression equation for CRM adults between leaf (X) and fruit (Y) was ln(Y+1) : 1.029 ln(X+1) ( $r^2$ : 0.80). The density of CRM was higher on fruit than on leaf according to fruit maturing level. TPL provided better description of mean-variance relation-ship for the dispersion indices compared to IPR. Slopes and intercepts of TPL from leaf and fruit samples did not differ between sample units and surveyed years. Fixed-precision levels (D) of a sequential sampling plan were developed using Taylor's power law parameters generated from adults of CRM in leaf sample. Sequential sampling plans for adults of CRM were developed for decision making CRM population level based on the different action threshold levels (2.0,2.5 and 3.0 mites per leaf) with 0.25 precision. The maximum number of trees and required number of trees sampled on fixed sample size plan on 2.0,2.5 and 3.0 thresholds with 0.25 precision level were 19, 16 and 15 and their critical values T$_{critical}$ at were 554,609 and 659, respectively. were 554,609 and 659, respectively.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.49-66
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• 2018

The purpose of this study is to analyze neighborhood environmental factors affecting bicycle accidents and accidental severity in Seoul, Korea. The use of bicycles has increased rapidly as daily transportation means in recent years. As a result, bicycle accidents are also steadily increasing. Using Traffic Accident Analysis System (TAAS) data from 2015 to 2017, this study uses negative binomial regression analysis to identify neighborhood environmental factors affecting bicycle accidents and accidential severity. The main results are as follows. First, bicycle accidents are more likely to occur in commercial and mixed land use areas where pedestrians, bicycle and vehicles are moving together. Second, bicycle accidents are positively associated with road structures such as four-way intersection. In contrast, three-way intersection is negatively associated with serious bicycle accidents. The density of speed hump or street tree is negatively associated with bicycle accidents and accidential severity. This finding indicates the effect of speed limit or street trees on bicycle safety. Fourth, bicycle infrastructures are also important factors affecting bicycle accidents and accidential severity. Bicycle-exclusive roads or bicycle-pedestrian mixed roads are positively associated with bicycle accidents and accidential severity. Finally, this study suggests policy implications to improve bicycle safety.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.5
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• pp.97-104
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• 2017

Greenspace enlargement through proper landscape planting is essential to creating a low carbon society. This study analyzed changes in stem diameter growth rates(DGR), ratios of below ground/above ground biomass(B/A), and carbon sequestration by age of major landscape tree species. Landscape trees for study were 11 species and 112 individuals planted in middle region of Korea. The DGR and B/A were analyzed based on data measured through a direct harvesting method including root digging. The carbon sequestration by tree age was estimated applying the derived regression models. The annual DGR at breast height of trees over 30 years averaged 0.72 cm/yr for deciduous species and 0.83 cm/yr for evergreen species. The B/A of the trees over 30 years averaged 0.23 for evergreen species and 0.40 for deciduous species, about 1.7 times higher than evergreen species. The B/A by age in this study did not correspond to the existing result that it decreased as tree ages became older. Of the study tree species, cumulative carbon sequestration over 25 years was greatest with Zelkova serrata(198.3 kg), followed by Prunus yedoensis(121.7 kg), Pinus koraiensis(117.5 kg), and Pinus densiflora (77.4 kg) in that order. The cumulative carbon sequestration by Z. serrata offset about 5% of carbon emissions per capita from household electricity use for the same period. The growth rates and carbon sequestration for landscape trees were much greater than those for forest trees even for the same species. Based on these results, landscape planting and management strategies were explored to improve carbon sequestration, including tree species selection, planting density, and growth ground improvement. This study breaks new ground in discovering changes in growth and carbon sequestration by age of landscape trees and is expected to be useful in establishing urban greenspaces towards a low carbon society.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.83-86
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• 2000

본 논문에서는 비정형 단위를 사용한 음성 합성 시스템의 합성음에 대한 자연성을 향상시키기 위한 휴지 구간 추출 및 휴지 지속시간 예측 모델을 제안한다. 제안된 휴지 지속시간 예측 모델은 트리 기반 모델링 기법 중 하나인 CART (Classification And Regression Trees)방법을 이용하였다. 이를 위해 남성 단일 화자가 발성한 6,220개의 어절경계 포함하는 총 400문장의 문 음성 데이터베이스를 구축하였고, 이 데이터베이스로부터 V-fold Cross-Validation 방법에 의해 최적의 트리를 결정하였다. 이 모델을 평가한 결과, 휴지 구간 추출 정확율은 $81\%$로 휴지 구간 존재 추출 정확율은 $83\%, 휴지 구간 비존재 추출 정확율은 $80\%이었고, 실 휴지지속시간과 예측 휴지지속시간과의 다중상관 계수는 0.84로, 오차 범위 20ms 이내에서 의 정 확율은 $88\%$ 이었다. 또한, 휴지지속시간을 예측하여 적용한 합성음을 청취 실험한 결과 자연 음성과 대체적으로 유사하게 나타났다.