• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.107-115
• /
• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Journal of Ecology and Environment
• /
• v.31 no.2
• /
• pp.125-129
• /
• 2008

To predict Daphnia secondary productivity along a trophic gradient indexed as total phosphorus (TP) concentration, we estimated energy transfer efficiencies from food quality for Daphnia such as eicosa-pentaenoic acid (EPA) or docosahexaenoic acid (DHA) content. Eleven flow-through Daphnia magna growth experiments were conducted with seston from 9 lakes, ponds and river waters. Primary productivities were estimated from food supply rates in the flow-through experiments, producing energy transfer efficiencies from seston to D. magna. We found DHA content was the best predictor of energy transfer efficiencies among the essential fatty acids. An asymptotic saturation model explained 79.6% of the variability In energy transfer efficiencies. Based on empirical data in this study and empirical models from literature, we predict that Daphnia productivity would peak in mesotrophic systems by decreasing food quality and Increasing food quantity along trophic gradient.

• Genomics & Informatics
• /
• v.5 no.4
• /
• pp.168-173
• /
• 2007

In previous nuclear genomic association studies, Random Forests (RF), one of several up-to-date machine learning methods, has been used successfully to generate evidence of association of genetic polymorphisms with diseases or other phenotypes. Compared with traditional statistical analytic methods, such as chi-square tests or logistic regression models, the RF method has advantages in handling large numbers of predictor variables and examining gene-gene interactions without a specific model. Here, we applied the RF method to find the association between mitochondrial single nucleotide polymorphisms (mtSNPs) and diabetes risk. The results from a chi-square test validated the usage of RF for association studies using mtDNA. Indexes of important variables such as the Gini index and mean decrease in accuracy index performed well compared with chi-square tests in favor of finding mtSNPs associated with a real disease example, type 2 diabetes.

• Structural Engineering and Mechanics
• /
• v.45 no.4
• /
• pp.521-542
• /
• 2013

In this study a truss model is used for the geometrically nonlinear static and dynamic analysis of a thin shallow arch subject to snap-through. Thanks to the very simple geometry of a truss, the equilibrium conditions can be easily written and the global stiffness matrix can be easily updated with respect to the deformed structure, within each step of the analysis. A very coarse discretization is applied; so, in a very simple way, the high frequency modes are suppressed from the beginning and there is no need to develop a complicated reduced-order technique. Two short computer programs have been developed for the geometrically nonlinear static analysis by displacement control of a plane truss model of a structure as well as for its dynamic analysis by the step-by-step time integration algorithm of trapezoidal rule, combined with a predictor-corrector technique. These two short, fully documented computer programs are applied on the geometrically nonlinear static and dynamic analysis of a specific thin shallow arch subject to snap-through.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2521-2525
• /
• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.2
• /
• pp.29.4-29.4
• /
• 2016

The relations between star formation and properties of molecular clouds are studied based on a sample of star forming regions in the Galactic Plane. Sources were selected by having radio recombination lines to provide identification of associated molecular clouds and dense clumps. Radio continuum and mid-infrared emission were used to determine star formation rates, while 13CO and submillimeter dust continuum emission were used to obtain masses of molecular and dense gas, respectively. We test whether total molecular gas or dense gas provides the best predictor of star formation rate. We also test two specific theoretical models, one relying on the molecular mass divided by the free-fall time, the other using the free-fall time divided by the crossing time. Neither is supported by the data. The data are also compared to those from nearby star forming regions and extragalactic data. The star formation "efficiency," defined as star formation rate divided by mass, spreads over a large range when the mass refers to molecular gas; the standard deviation of the log of the efficiency decreases by a factor of three when the mass of relatively dense molecular gas is used rather than the mass of all the molecular gas.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.172.4-172
• /
• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• YAKHAK HOEJI
• /
• v.59 no.4
• /
• pp.151-157
• /
• 2015

The purpose of this study is to demonstrate how lead compounds are best optimized with the application of in silico QSAR and PBPK modeling at the early drug discovery stage. Several predictive QSAR models such as $IC_{50}$ potency model, intrinsic clearance model and brain penetration model were built and applied to a set of virtually synthesized library of the BACE1 inhibitors. Selected candidate compounds were also applied to the PBPK modeling for comparison between the predicted animal pharmacokinetic parameters and the observed ones in vivo. This novel lead optimization strategy using QSAR and PBPK modelings could be helpful to expedite the drug discovery process.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.249-261
• /
• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of Mechanical Science and Technology
• /
• v.19 no.10
• /
• pp.1875-1884
• /
• 2005

This paper discusses Neural Networks as predictor for analyzing of transmission angle of slider-crank mechanism. There are different types of neural network algorithms obtained by using chain rules. The neural network is a feedforward neural network. On the other hand, the slider-crank mechanism is a modified mechanism by using an additional link between connecting rod and crank pin. Through extensive simulations, these neural network models are shown to be effective for prediction and analyzing of a modified slider-crank mechanism's transmission angle.