• Journal of Korean Society of Forest Science
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• v.101 no.2
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• pp.213-219
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• 2012

The $5^{th}$ national forest inventory (NFI5) has been reorganized to annual inventory system for providing multi-resources forest statistics at a point in time. The objective of this study is to evaluate statistical estimators for estimating forest growing stock in Chungcheongbuk-Do from annual inventory data. When comparing two estimators; simple random sampling (SRS) and double sampling for post-stratification (DSS), for estimating mean forest growing stock ($m^3/ha$) at each surveyed year, the estimate for DSS in which a population of interest is stratified into three sub-population (forest cover types) was more precise than that for SRS. To combine annual inventory field data, three estimators (Temporally Indifferent Method; TIM, Moving Average; MA, and Weighted Moving Average; WMA) were compared. Even though the estimated mean for TIM and WMA is identical, WMA-DSS is preferred to provide more smaller variance of estimated mean and to adjust for catastrophic events at a surveyed year (so-called "lag bias") by annual inventory data.

• Journal of agriculture & life science
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• v.50 no.4
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• pp.45-57
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• 2016

The average income of forestry household was only 54.3% of urban workers's and 67.6% of farms household's income based on the data from statistics Korea in 2012. This indicates that forestry, which is a labor-intensive primary industry, has the limitation for creating added value. On the other hand, the demands for forest healing and forest experiential program have been continuously increased with new lifestyle focusing on the quality of life and increased leisure time. Therefore, it is necessary to establish comprehensive policies to increase added value except forestry to respond forest demands. The project utilizing forest healing can be on of solutions to meet forest demands. Thus, this research intends to investigate an economic revitalization plan for mountain villages with forest healing. The characteristics of forest healing facilities and contents of forest healing programs were examined through internet searching, fields surveys, and expert interviews. Total 186 concepts, 8 categories, and 24 subcategories were derived from raw data of surveys. The application process of forest healing was also provided to encourage local economy of mountain areas. This research offers application procedure of the forest healing for regional economic vitalization in Mountain Villages interviews using grounded theory by Strauss and Corbin(1988) as well as NVio11. This research contributes to prepare the base of future quantitative studies by providing strategies and suggestions for the application plans of forest healing programs. In addition, this research offers basic data for the policies to establish and manage forest healing villages.

• International Journal of Reliability and Applications
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• v.6 no.2
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• pp.79-85
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• 2005

We show how comparative mean residual life functions (MRL) can be used to give unique insights into strengths of materials data. Recall that Weibull's original reliability function was developed studying and fitting strengths for various materials. This creative comparing of MRL functions approach can be used for regular life data or any time to response data. We apply graphical MRL's to real data from tests of tensile strength of high quality engineered wood.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.411-422
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• 2014

A classification ensemble method aggregates different classifiers obtained from training data to classify new data points. Voting algorithms are typical tools to summarize the outputs of each classifier in an ensemble. WAVE, proposed by Kim et al. (2011), is a new weight-adjusted voting algorithm for ensembles of classifiers with an optimal weight vector. In this study, when constructing an ensemble, we applied the WAVE algorithm on the double-bagging method (Hothorn and Lausen, 2003) to observe if any significant improvement can be achieved on performance. The results showed that double-bagging using WAVE algorithm performs better than other ensemble methods that employ plurality voting. In addition, double-bagging with WAVE algorithm is comparable with the random forest ensemble method when the ensemble size is large.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.105-112
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• 2014

The main purpose of this study is to measure spatio-temporal variation of forest tree volume based on the RCP(Representative Concentration Pathway) 8.5 scenario, targeting on Pinus densiflora forests which is the main tree species in South Korea. To estimate nationwide scale, $5^{th}$ forest type map and National Forest Inventory data were used. Also, to reflect the impact of change in place and climate on growth of forest trees, growth model reflecting the climate and topography features were applied. The result of the model validation, which compared the result of the model with the forest statistics of different cities and provinces, showed a high suitability. Considering the continuous climate change, volume of Pinus densiflora forest is predicted to increase from $131m^3/ha$ at present to $212.42m^3/ha$ in the year of 2050. If the climate maintains as the present, volume is predicted to increase to $221.92m^3/ha$. With the climate change, it is predicted that most of the region, except for some of the alpine region, will have a decrease in growth rate of Pinus densiflora forest. The growth rate of Pinus densiflora forest will have a greater decline, especially in the coastal area and the southern area. With the result of this study, it will be possible to quantify the effect of climate change on the growth of Pinus densiflora forest according to spatio-temporal is possible. The result of the study can be useful in establishing the forest management practices, considering the adaptation of climate change.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.323-331
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• 2021

With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO₂, NH₃, H₂S, CH₄) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.73-85
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• 2016

Systematic and efficient forestry management is required because of the long-term low profitability of timber production and forest products. In this situation, non-timber forest products can be the solution to secure stable sources of income for workers in the forestry field. However, the existing studies for non-timber forest products focus on effective production and economic analysis and provide plans for increasing the income and improving the marketing system. Therefore, this research intends to analyze the spatial distribution as well as quantitative concentration of non-timber forest production. To achieve this goal, this study examined the regional concentration and dispersion of non-timber forest production in 2001, 2007, and 2014 using the coefficient of localization(CL) and location quotient(LQ) and investigated the change in spatial distribution using spatial statistics. The production of chestnuts generally showed a concentrated pattern in 2014 based on the outputs of the CL and LQ, but the result of spatial autocorrelation indicated a decrease in the spatial concentration. In addition, astringent persimmon showed more concentration from the output of CL than oak mushroom, but Moran's I suggests the opposite. Therefore, it is necessary to examine the spatial distribution to understand and improve the marketing system and intensify the production of forest products.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.4
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• pp.247-254
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• 2003

This study was conducted to develop site index equations by main species grown in Gyunggi and Chungcheong provinces using environmental factors obtained from a digital forest site map. For this, 28 environmental factors were regressed on site index by species. Four to five environmental factors by species were selected as independent variables in the best site index equations (coefficients of determination greater than 0.91). For these site index equations, three evaluation statistics, mean difference, standard deviation of difference, and standard error of difference, were applied to the data set. Site index equations by species relationships developed in this study effectively estimate forest productivity in the study area. However, the site index equation of Larix leptolepis showed a larger than expected bias between the estimated and the measured site index. The reason is not clear in this situation, but might be because of the small sample set. It will be necessary, therefore, to conduct more studies to determine the exact reason. It is also expected that the site index equations with a few environmental factors as independent variables could provide valuable information about species well suited to given site conditions. Site index equations for other species should be developed to establish a rational policy about the selection of best species for site conditions.