• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.851-861
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• 2022

In this study, we analyzed the effects of trees planted in urban areas on PM_2.5 reduction using a computational fluid dynamics (CFD) model. For realistic numerical simulations, the meteorological components(e.g., wind velocity components and air temperatures) predicted by the local data assimilation and prediction system (LDAPS), an operational model of the Korea Meteorological Administration, were used as the initial and boundary conditions of the CFD model. The CFD model was validated against, the PM_2.5 concentrations measured by the sensor networks. To investigate the effects of trees on the PM_2.5 reduction, we conducted the numerical simulations for three configurations of the buildings and trees: i) no tree (NT), ii) trees with only drag effect (TD), and iii) trees with the drag and dry-deposition effects (DD). The results showed that the trees in the target area significantly reduced the PM_2.5 concentrations via the dry-deposition process. The PM_2.5 concentration averaged over the domain in DD was reduced by 5.7 ㎍ m^-3 compared to that in TD.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Korean Journal of Environment and Ecology
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• v.25 no.1
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• pp.47-56
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• 2011

The research was conducted to find optimal habitats of warm-temperate evergreen broad-leaved trees, and to investigate climate factors to determine their distribution using classification tree (CT) analysis. The warm-temperate evergreen broad-leaved trees model (EG-model) constructed by CT analysis showed that Mean minimum temperature of the coldest month (TMC) is a major climate factor in determining distribution of warm-temperate evergreen broad-leaved trees. The areas above the $-5.95^{\circ}C$ of TMC revealed the optimal habitats of the trees. The coldest month mean temperature (CMT) equitable to $-5.95^{\circ}C$ of TMC is $-1.7^{\circ}C$, which is lower than $-1^{\circ}C$ of CMT of warm-temperate evergreen broad-leaved trees. Suitable habitats were defined for warm-temperate evergreen broad-leaved trees in Korea. These habitats were classified into two areas according to the value of TMC. One area with more than$-5.95^{\circ}C$ of TMC was favorable to trees if the summer precipitation (PRS) is above 826.5mm; the other one with less than $-5.95^{\circ}C$ of TMC was favorable if PRS is above 1219mm. These favorable conditions of habitats were similar to those of warm-temperate evergreen broad-leaved trees in Japan. We figured out from these results that distribution of warm-temperate evergreen broad-leaved trees were expanded to inland areas of southern parts of Korean peninsula, and ares with the higher latitude. Finally, the northern limits of warm-temperate evergreen broad-leaved trees might be adjusted accordingly.

• Journal of the Korean Mathematical Society
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• v.54 no.3
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• pp.987-997
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• 2017

A deterministic model for the spread of pine wilt disease with asymptomatic carrier trees in the host pine population is designed and rigorously analyzed. We have taken four different classes for the trees, namely susceptible, exposed, asymptomatic carrier and infected, and two different classes for the vector population, namely susceptible and infected. A complete global analysis of the model is given, which reveals that the global dynamics of the disease is completely determined by the associated basic reproduction number, denoted by $\mathcal{R}_0$. If $\mathcal{R}_0$ is less than one, the disease-free equilibrium is globally asymptotically stable, and in such a case, the endemic equilibrium does not exist. If $\mathcal{R}_0$ is greater than one, the disease persists and the unique endemic equilibrium is globally asymptotically stable.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.561-589
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• 2022

When multiple classifications and regression trees are combined, tree-based ensemble models, such as random forest (RF) and Bayesian additive regression trees (BART), are produced. We compare the model structures and performances of various ensemble models for regression settings in this study. RF learns bootstrapped samples and selects a splitting variable from predictors gathered at each node. The BART model is specified as the sum of trees and is calculated using the Bayesian backfitting algorithm. Throughout the extensive simulation studies, the strengths and drawbacks of the two methods in the presence of missing data, high-dimensional data, or highly correlated data are investigated. In the presence of missing data, BART performs well in general, whereas RF provides adequate coverage. The BART outperforms in high dimensional, highly correlated data. However, in all of the scenarios considered, the RF has a shorter computation time. The performance of the two methods is also compared using two real data sets that represent the aforementioned situations, and the same conclusion is reached.

• The Plant Pathology Journal
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• v.37 no.6
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• pp.543-554
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• 2021

To preventively control fire blight in apple trees and determine policies regarding field monitoring, the Maryblyt ver. 7.1 model (MARYBLYT) was evaluated in the cities of Chungju, Jecheon, and Eumseong in Korea from 2015 to 2020. The number of blossom infection alerts was the highest in 2020 and the lowest in 2017 and 2018. And the common feature of MARYBLYT blossom infection risks during the flowering period was that the time of BIR-High or BIR-Infection alerts was the same regardless of location. The flowering periods of the trees required to operate the model varied according to the year and geographic location. The model predicts the risk of "Infection" during the flowering periods, and recommends the appropriate times to control blossom infection. In 2020, when flower blight was severe, the difference between the expected date of blossom blight symptoms presented by MARYBLYT and the date of actual symptom detection was only 1-3 days, implying that MARYBLYT is highly accurate. As the model was originally developed based on data obtained from the eastern region of the United States, which has a climate similar to that of Korea, this model can be used in Korea. To improve field utilization, however, the entire flowering period of multiple apple varieties needs to be considered when the model is applied. MARYBLYT is believed to be a useful tool for determining when to control and monitor apple cultivation areas that suffer from serious fire blight problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.72-75
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• 2006

Event trees are widely used graphical tool to denote the accident inintiation and escalation to more severe accident. But they have some drawbacks in that they do not have efficient way of updating model parameters and also they can not contain the information about dependency or independency among model parameters. A tool that can cure such drawbacks is an influence diagram. We introduce influence diagrams and explain how to update model parameters and obtain predictive distributions. We show that an event tree can be converted to a statistically equivalent influence diagram, and bayesian prediction can be made more effectively through the use of influence diagrams.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1174-1176
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• 2003

Recently 3D city models are required for many applications such as urban microclimate, transportation navigation, landscape planning and visualization to name a few. The existing 3D city models mostly target on modeling buildings, but vegetation also plays an important role in the urban environment. To represent a more realistic urban environment through the 3D city model, in this research, an investigation is conducted to extract the position of trees from high resolution IKONOS imagery along with Airborne Laser Scanner data. Later, a tree growth model is introduced to simulate the growth of trees in the identified tree-positions.

• Wind and Structures
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• v.22 no.1
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• pp.89-106
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• 2016

With an aim to assess the wind damage to urban trees in more realistic conditions, the nonlinear dynamics of structured trees subjected to strong winds with different levels is investigated in the present paper. For the logical treatment of dynamical behavior of trees, material nonlinearities of green wood associated with tree biomechanics and geometric nonlinearity of tree configuration are included. Applying simulated fluctuating wind velocity to the numerical model, the dynamical behavior of the structured tree is explored. A comparative study against the linear dynamics analysis usually involved in the previous researches is carried out. The failure wind velocity of urban trees is then defined, whereby the failure percentages of the tree components are exposed. Numerical investigations reveal that the nonlinear dynamics analysis of urban trees results in a more accurate solution of wind-induced response than the classical linear dynamics analysis, where the nonlinear effect of the tree behavior gives rise to be strengthened as increasing of the levels of wind velocity, i.e., the amplitude of 10-min mean wind velocity. The study of relationship between the failure percentage and the failure wind velocity provides a new perspective towards the vulnerability assessment of urban trees likely to fail due to wind actions, which is potential to link with the practical engineering.

• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.860-872
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• 2002

Fault tree analysis is the most widely used saftly analysis technique in industry. However, the analysis is often applied manually, and there is no systematic and automated approach available to validate the analysis result. In this paper, we demonstrate that a real-time model checker UPPAAL is useful in formally specifying the required behavior of safety-critical software and to validate the accuracy of manually constructed fault trees. Functional requirements for emergency shutdown software for a nuclear power plant, named Wolsung SDS2, are used as an example. Fault trees were initially developed by a group of graduate students who possess detailed knowledge of Wolsung SDS2 and are familiar with safety analysis techniques including fault tree analysis. Functional requirements were manually translated in timed automata format accepted by UPPAAL, and the model checking was applied using property specifications to evaluate the correctness of the fault trees. Our application demonstrated that UPPAAL was able to detect subtle flaws or ambiguities present in fault trees. Therefore, we conclude that the proposed approach is useful in augmenting fault tree analysis.