• Journal of Korean Society of Forest Science
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• v.105 no.3
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• pp.336-341
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• 2016

The object of this study was to classify the local types in relation to regional differences using Height-DBH growth of Pinus densiflora in Korea. The regional types were clustered according to Getis-Ord's $G_i$ among Local indicators of spatial association (LISA) by characteristics of spatial distribution which were calculated the residual of sample plots by fitting Height-DBH growth model using Weibull growth equation. Accordingly, Pinus densiflora were classified 3 groups, It indicated that annual precipitation had one of the biggest impacts among the considered site and climate factors. This results can become the standard for regional management of Pinus densiflora forests.

• Fire Science and Engineering
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• v.25 no.5
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• pp.39-46
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• 2011

In order to consider the prevention countermeasure to the occurrence of forest fires, analysing characteristic of the past forest fire data is needed. This research analyzed distribution and characteristics of forest fires damage over 30 ha based on statistics data of forest fires in Korea between 1975 and 2010. As a result, the number of forest fires damage over 30 ha as 23 was most occurred in 1978. Forest fires show an upward tendency from 1970 to 2000. Forest fires of 30 ha~50 ha damaged area was most occurred. Forest fire in Gangwon province was occurred as the number of total 66 (37.0 %). Gangwon province was superior in point density analysis. The number of forest fire occurrence over 30 ha was most high to 114 (63.0 %) in April and to 44 (24.3 %) in Sunday. The occurrence number of forest fire and damage caused by forest fire is increasing more and more since 1975, appropriate authorities can use effectively in devising policy for forest fire prevention from this result.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1133-1146
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• 2015

The Horse race industry has the largest proportion of the domestic legal gambling industry. However, there is limited statistical analysis on horse races versus other sports. We propose prediction models for winning horses in horse races using data mining techniques such as logistic regression, linear regression, and random forest. Horse races data are from the Korea Racing Authority and we use horse racing reports, information of racehorses, jockeys, and horse trainers. We consider two models based on ranks and time records. The analysis results show that prediction of ranks is affected by information on racehorses, number of wins of racehorses and jockeys. We place wagers for the last month of races based on our prediction models that produce serious profits.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.933-949
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• 2015

Audience rating competition in the domestic drama market has increased recently due to the introduction of commercial broadcasting and diversification of channels. There is now a need for thorough studies and analysis on audience rating. Especially, a drama rating is an important measure to estimate advertisement costs for producers and advertisers. In this paper, we study the drama rating prediction models using various data mining techniques such as linear regression, LASSO regression, random forest, and gradient boosting. The analysis results show that initial drama ratings are affected by structural elements such as broadcasting station and broadcasting time. Average drama ratings are also influenced by earlier public opinion such as the number of internet searches about the drama.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.5
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• pp.53-59
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• 2012

Each year, the Forest Service performs measurements to gather statistics regarding on the forest resources and forest character. However, this is not easily obtainable information due to the lack of human accessibility to the survey sample. I proposed a new method to gather data which utilizes the technology of digital imaging. This new method allows over 50% of the sample to be viewable.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.23-27
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• 2023

The purpose of this study is to identify and analyze suicide factors of adolescents using the Random Forest algorithm. According to statistics on the cause of death by the National Statistical Office in 2019, suicide was the highest cause of death in the 10-19 age group, which is a major social problem. Using machine learning algorithms, research can predict whether individual adolescents think of suicide without investigating suicidal ideation and can contribute to protecting adolescents and analyzing factors that affect suicide, establishing effective intervention measures. As a result of predicting with the random forest algorithm, it can be said that the possibility of identifying and predicting suicide factors of adolescents was confirmed. To increase the accuracy of the results, continuous research on the factors that induce youth suicide is necessary.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.163-166
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• 2001

One of the most important factors influencing the outbreak and severity of foliar diseases is the duration of wetness from dew deposition, rainfall, or irrigation. Models may provide good alternatives for assessing leaf wetness duration (LWD) without the labor, cost, and inconvenience of making measurements with sensors.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.93-96
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• 2001

Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.238-238
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• 2019

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.504-511
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• 2020

This study estimated planned felling volumes to set targets for management planning of nationwide country-owned forests. Estimates were made using timber harvest prediction methods that use probability density functions, including area weighting (AW), area ratio weighting (ARW), and sample area change ratio weighting (SCRW). Country-owned forest areas in 2010 and 2015 were used to estimate planned felling volumes, as shown in basic forest statistics, and calculations were made assuming that the felling areas were the changes in the forest area over the 5-year period. For the age classes of V-VI, the average felling ages for AW, ARW, and SCRW were 5.41, 5.56, and 5.37, respectively, and the felling areas were 594,462, 586,704, and 580,852 ha, respectively, with ARW reaching closest to the actual changes. The actual changes in the areas and chi-squared test results were most stable with the SCRW method. This study showed that SCRW was more adequate than AW and ARW as a method to predict timber harvests for forest management planning.