• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.54-63
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• 2006

The number of forest fire under various conditions such as year, month, time, day of the week, region, damaged species, cause, and damaged area are checked, and the statistics of the forest fire causing materials in recent 14 years ('91-'04) are analyzed. The result shows that the year majority of forest fires had happened in last 14 year was 2001 and most of forest fire occurred in April, Sunday, around 14:00 to 15:00. The most damaged region is Gyeongsangbuk-Do, followed by Gangwon-Do, Jeollabuk-Do, and Gyeonggi-Do. The most damaged species is pine tree. The main causes of forest fires are accidental fire and incineration of a field boundary; however, recently, incendiarism is increased. The result of analysis on the damaged area shows that small fires under 5 ha occurred most frequently and large fires (over 30 ha) occurred mostly in Kangwon province (44.2%). The result also shows that the large forest fires (1,113 minutes) require 7.5 time more than the small forest fires (148 minutes). Especially, since average damaged area caused by large forest fire was about 470 ha per incident.

• Journal of Forest and Environmental Science
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• v.22 no.1
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• pp.1-12
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• 2006

This study was carried out to compile year 2001 forest resource statistics for the State of Virginia. USA. Virginia has 15.8 million acres (6.4 million ha) of forested 1and, accounting for 62% of the landcover with non-industrial private forest landowners owning 77% of the forested area. Deciduous forests make up 78% of Virginia's forests. Total tree volume is 26.5 billion cubic ft, of which average volume per acre is $1.677ft^3/ac(117m^3/ha)$. The overall annual volume of roundwood output is $543\;million\;ft^3$. Tree growth exceeds removals by $271\;million\;ft^3$ each year for all species statewide. Average net forest land loss in Virginia is 20,000 acre (8,094 ha) per year. In 1999, the forest products industry contributed over $25.4 billion to Virginia's economy while providing over 248,000 jobs. Among forest industries logging contributes to the economy at over $863 million/yr; timber accounts for the greatest amount (28%) of the total market value of Virginia's agricultural crops. Revenue received from stumpage by landowners exceeded $345 million/yr. In their entirety. Virginia's forests provide over $30.5 billion in annual return. including $3 billion for recreation and $1.9 billion for carbon sequestation and pollution control.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.455-465
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• 2011

In order to assess and mitigate climate change, the role of forest biomass as carbon sink has to be understood spatially and quantitatively. Since existing forest statistics can not provide spatial information about forest resources, it is needed to predict spatial distribution of forest biomass under an alternative scheme. This study focuses on developing an upscaling method that expands forest variables from plot to landscape scale to estimate spatially explicit aboveground biomass(AGB). For this, forest stand variables were extracted from National Forest Inventory(NFI) data and used to develop AGB regression models by tree species. Dominant/codominant height and crown density were used as explanatory variables of AGB regression models. Spatial distribution of AGB could be estimated using AGB models, forest type map and the stand height map that was developed by forest type map and height regression models. Finally, it was estimated that total amount of forest AGB in Danyang was 6,606,324 ton. This estimate was within standard error of AGB statistics calculated by sample-based estimator, which was 6,518,178 ton. This AGB upscaling method can provide the means that can easily estimate biomass in large area. But because forest type map used as base map was produced using categorical data, this method has limits to improve a precision of AGB map.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.29-37
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• 2017

It has gradually become important to estimate a forest stand volume utilizing LiDAR data. Recently, various statistical models including a linear regression model has been introduced to estimate a forest stand volume using LiDAR data. One of limitations of the current approaches is in that the accuracy of observed forest stand volume data, which is used as a response variable, is questionable unstable. To overcome this limitation, we consider a spatial structure for a forest stand volume. In this research, we propose a hierarchical model for applying a spatial structure to a forest stand volume. The proposed model is applied to the LiDAR data and the forest stand volume for Bonghwa, Gyeongsangbuk-do.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.255-264
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• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.

• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.285-291
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• 2010

Since 2006, the $5^{th}$ National Forest Inventory (NFI) has been implemented to provide forest resources statistics at the national level and at the county level as well. However, it needs a small-area estimator for estimating forest statistics at the county-level due to a small number of samples collected within a county. This study was conducted to evaluate the applicability of a geographical-based synthetic estimator for estimating forest growing stock volumes at the county level. The NFI-field plots surveyed were post-stratified into three forest cover types. In the synthetic estimator, field plots within a geographical-based super-county for each county were used to estimate stratum weights and stratum mean volumes. It was resulted that estimated stratum weights using the synthetic estimation were significantly differ from forest cover maps. The standard errors of estimated mean by the synthetic estimation that ranged from ${\pm}3.5\;m^3$/ha to ${\pm}7.7\;m^3$/ha were more smaller than those (${\pm}7.8\;m^3/ha{\sim}{\pm}24.7\;m^3/ha$) by the direct estimation. This means that the synthetic estimation is possible to provide more precise estimates of mean volumes.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.53-70
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• 2008

In this paper, we discuss the plethora of uses for the software package R, and focus specifically on its helpful applications in reliability data analyses. Examples are presented; including the R coding protocol, R code, and plots for various statistical as well as reliability analyses. We explore Kaplan-Meier estimates and maximum likelihood estimation for distributions including the Weibull. Finally, we discuss future applications of R, and usages of quantile regression in reliability.

• International Journal of Reliability and Applications
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• v.4 no.4
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• pp.157-170
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• 2003

In this paper we apply statistical reliability tools to manage and seek improvements in the strengths of medium density fiberboard (MDF). As a part of the MDF manufacturing process, the product undergoes destructive testing at various intervals to determine compliance with customer′s specifications. Workers perform these tests over sampled cross sections of the MDF panel to measure the internal bond (IB) in pounds per square inches until failure. We explore both graphically and statistically this "pressure-to-failure" of MDF. Also, we briefly comment on reducing sources of variability in the IB of MDF.