• Journal of Korean Society of Forest Science
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• v.89 no.2
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• pp.223-231
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• 2000

This study aims to clarify the effect of forest management practices(thinning and pruning) on water quality to get the fundamental information on the facility of cleansing water quality after forest operation. Rainfall, throughfall and stemflow were sampled at the study sites which consist of Abies holophylla and Pinus koraiensis in Kwangnung Experimental Forest for 6 months from May 4, to November 1, 1999. Average tree height in the management sites increased by 0.5m more than that in the non-management sites in Abies holophylla and Pinus koraiensis, respectively. Increment of average D.B.H. at the management sites grew 3.5cm and 2.6cm more in Abies holophylla and Pinus koraiensis compared with that at non-management sites. Average pH of the total amount for the event in throughfall and stemflow was higher than that of throughfall and stemflow of the beginning of the event at the management and non-management sites. Average electrical conductivity of throughfall and stemflow at the beginning of the event was higher than that of the total amount for the event at management and non-management sites. Water qualities of throughfall and stemflow were buffered more by the management practice in both. The forest management may affect purification of water quality of throughfall and stemflow in Abies holophylla and Pinus koraiensis.

• Korean Journal of Environment and Ecology
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• v.27 no.6
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• pp.718-732
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• 2013

The purpose of this study was to provide data for the basic research to found the effective conservation and management plan for the Geumdangsil Pine Forest of Yecheon designated as Natural Monument No. 469. Furthermore, this paper suggest efficient sustainable forest preservation and using. In order to achieve the sustainable forest preservation, this study was to analyse topography, land use, tree growth, soil environment, forest usage and forest management, etc. According to analysis the results, the site area is located in the flatlands where is from 130 to 140 m above sea level. The around forest was transformed into agricultural land. The 565 individuals of Pinus densiflora grows in the forest, whereas, 25 trees was cut down or died. There are signs of 25 stumps. The most of 565 trees' diameter at breast height(DBH) was centerized between 30 cm and 50 cm, moreover, the average life expectancy of trees were 85.4 years. The oldest age of tree was estimated to be 200 years. The Sample trees of rate of branch growth is from 4.3 cm to 5.1 cm per year. The middle branch which is more vigorous growth grow 24.2 cm for 3 years. Moreover, the result of soil physico-chemical properties analysis of 7 plots, 4 categories which is soil organic matter, total nitrogen, available phosphoric acid, specific electrical conductance was generally good, however, the 2 categories which is soil pH, exchangeable cation needed improvement. Currently, the site was not pressured by facilities and usage, however, there might be threaten by agriculture such as encroaching on forest. Therefore, there should establish comprehensive ecosystem management such as facility management, visitors management and operation management In this paper considered 4 fields that is ecosystem management, facility management, visitors management and operation management for sustainable management.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.1
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• pp.59-70
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• 2011

The purpose of this study is to present raw data to systematically and rationally manage the protected trees located in Changwon-si, Korea. This study investigated about the present condition and the information of location, individual, management, health and soil. The results are as follows. The protected trees were located in 26 spots, and species of trees were 9 taxa; Zelkova serrata, Celtis sinensis, Aphananthe aspera, Ginkgo biloba, Carpinus tschonoskii, Pinus densiflora for. multicaulis, Quercus variabilis, Pinus densiflora and Salix glandulosa. In protected tree types, shade trees were the most, and the majority of theirs were 200 years or more in age. The range of altitude was 14~173m, and the number of trees located in flat fields was the most. For location types, village and field and mountain were presented in the order and, in land use, land for building was the most. The range of height was 8.0~30.0m, 0.6~5.1m in crown height, 240~700cm in diameter of breast and 210~800cm in diameter of root. In case of crown area, Zelkova serrata of No.5 was most large. The status boards were mostly installed except No.23 and No.26. The sites with fence were 9 spots, and the site with stonework were 14 spots. The sites with the support beam were 5 spots, and most sites were not covered up with soil. The materials of bottom were soil, gravel and vegetation in the order. The range of withering branch rate was 0~40%, and peeled bark rate was 0~60%. The sites made holes were 23 spots, and the hole size of Aphananthe aspera of No.12 was the largest. The sites disturbed by human trampling were 7 spots, the sites by disease and insects of 2 spots, the sites by injury of 23 spots and the sites by exposed roots of 13 spots. In the results of soil analysis, there showed that acidity was pH 4.5~8.0, organic matter content of 3.5~69.8g/kg, electrical conductivity(EC) of 0.11~2.87dS/m, available $P_2O_5$ of 3.0~490.6mg/kg, exchangeable K of 0.10~1.05cmol+/kg, exchangeable Ca of 1.41~16.45cmol+/kg, exchangeable Mg of 0.37~1.96cmol+/kg, exchangeable Na of 0.25~2.41cmol+/kg and cation exchange capacity(C.E.C) of 8.35~26.55cmol+/kg.

• Korean Journal of Ecology and Environment
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• v.35 no.1 s.97
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• pp.52-61
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• 2002

This research was conducted to evaluate the effect of forest management practices on pH and electrical conductivity to get fundamental information on water purification capacity after forest operation. Rainfall, throughfall and stemflow　were　sampled at the study sites which consist of Abies holophylla and Pinus koraiensis　in Gwangreung Experimental Forest for S months from May to November 1999. Mean pH of the throughfall of the beginning of the event was higher in management (thinning and pruning) sites of Abies holophylla and Pinus koraiensis stands than nonmanagement site of Abies holophylla and Pinus koraiensis stands. In addition, pH of the throughfall of the total amount of the event showed similar trends which are higher pH in the management sites compared with the non- management sites. This result indicates that managements such as thinning and pruning improve tree butler capacity of rainfall pH. According to the linear regression results, pH of the throughfall of the total amount of the event in non-management sites = 0.735${\times}$pH of the throughfall of the beginning of the event in non-management sites+1.849 ($R^2\;=\;0.82$) and pH of the throughfall of the total amount of the event in management sites= 0.863${\times}$pH of the throughfall of the beginning of the event in management sites +1.0242 ($R^2\;=\;0.87$). In case of stemflow pH, pH of the sternflow of the total amount of　the event in non-management sites = 0.53${\times}$pH of the stemflow of the beginning of　the event in non- management sites+2.7709 ($R^2\;=\;0.64$) and pH of the stemflow of the total amount of the event in management sites = 0.5854${\times}$pH of the stemflow of the beginning of the event in management sites+2.7045 ($R^2\;=\;0.65$). Electrical conductivity (EC) of the throughfall of the beginning and total amount of the event was highest in non- management site in Abies holophylla, followed by management sites in fsies Abies holophylla, non-management site in Pinus koraiensis, and management sites in Pinus koraiensis stands, respectively. According to the linear regression results, EC of the throughfall of the total amount of the event in non-managementsites = 0.4045${\times}$EC of the throughfall of the beginning of the event in non-management sites+26.766 ($R^2\;=\;0.69$) and EC of the throughfall of the total amount of the event in management sites = 0.6002${\times}$EC of the throughfall of the beginning of the event in management sites+8.0184 ($R^2\;=\;0.54$). In case of stemflow EC, EC of thestemflow of the total amount of the event in non-management sites = 0.6298${\times}$EC of the stemflow of the beginning of the event in non-management sites+11.582 ($R^2\;=\;0.72$) and pH of the stemflow of the total amount of the event in management sites =0.602${\times}$pH of the stemflow of the beginning of the event in management sites+20.783($R^2\;=\;0.49$).

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.3
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• pp.77-91
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• 2015

This study aims to suggest adequate soil management through the analysis of physicochemical properties of soil in the planting grounds of Incheon International Airport, which was constructed on a massive land reclamation site. Study areas were 5 sites at the international business complex, the passenger terminal, the airport support complex, the free trade zone, and the access road. Soil profile analysis showed that 9 plots out of the 27 plots were hardpan and heterospere within 80cm from the soil surface. The earth laid on the ground was categorized as gravel based soil(4 plots), dredged soil from the sea bottom and mixed reclamation materials(2 plots), clay with poor permeability(3 plots) and waste construction material(1 plot). Average soil hardness was $11.5kg/cm^2$ and soil textures were sandy soil, sandy loam and loamy sand. Average soil pH was 6.7 and average organic matter content was 0.7%. Electrical conductivity was 0.0dS/m and exchangeable cation concentrations were $Ca^{2+}$ 3.4cmol/kg, $Mg^{2+}$ 1.5cmol/kg, $K^+$ 0.3cmol/kg and $Na^+$ 1.0cmol/kg. Average cation exchange capacity was 11.0cmol/kg. Although average figures in Solum mostly meet the landscape design criteria, properties of each soil layer showed various values sometimes over the limit. Base saturations were $Ca^{2+}$ 29.9%, $Mg^{2+}$ 13.3% and $K^+$ 3.7% for lower soil, $Ca^{2+}$ 33.3%, $Mg^{2+}$ 17.0% and $K^+$ 2.7% for mid-soil and $Ca^{2+}$ 32.6%, $Mg^{2+}$ 12.2% and $K^+$ 1.9% for upper soil. Exchangeable sodium percentages were 16.4% for lower soil, 7.5% for mid-soil and 4.7% upper soil. Sodium adsorption rates were 0.8 for lower soil, 0.3 for mid-soil and 0.2 for upper soil. Factors affecting to the vegetation growth were heterogeneity and poorness of solum, disturbance of dredged soils, high soil hardness including hardpan in the subsurface soil layer and shallow effective soil depth, high soil acidity, imbalance of base contents, low organic matter content and low available phosphate levels in the soil.

• Journal of Environmental Impact Assessment
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• v.24 no.4
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• pp.352-366
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• 2015

The purpose of this study is to offer the basic data to the habitat conservation and management by surveying and analysing the ecological characteristics such as the flora, vegetation structure and soil of Rhodotypos scandens habitat. The flora were summarized as 131 taxa including 57 families, 99 genera, 107 species, 1 subspecies, 17 varieties and 6 forms. The life forms grouped as in the follows; the megaphanerophytes(MM) were 21 taxa, 24 taxa of the microphanerophytes(M), 30 taxa of nanophanerophytes(N), 6 taxa of chamaiphytes(Ch), 20 taxa of hemicryptophytes(H), 17 taxa of geophytes(G), 12 taxa of therophytes(Th) and 1 taxa of hydrophytes(HH). The present conditions of communities were 8 types including Zelkova serrata, Quercus aliena, Ulmus parvifolia, Rhamnella frangulioides, Castanea crenata, Albizia julibrissin, Celtis sinensis and Robinia pseudoacacia. In case of the dominant species by layers, the tree layer species were Zelkova serrata, Quercus aliena, Ulmus parvifolia, Castanea crenata, Albizia julibrissin, Celtis sinensis and Robinia pseudoacacia, and the subtree layer species were Rhamnella frangulioides, Q. aliena, Z. serrata, A. julibrissin, U. parvifolia and Broussonetia kazinoki. The shrub layer species was Rhodotypos scandens. In the results of analyzing the diversity index, $H^{\prime}{_{max}}$ from 1.691 to 2.610, from 2.197 to 3.466 in H'max, from 0.646 to 0.903 in J' and from 0.097 to 0.354 in D. In the results of analysing the soil, there showed that acidity was 5.6, 0.5dS/m of electrical conductivity(EC), 59.1mg/kg of available $P_2O_5$, 49.7% of organic matter content, $0.4cmol^+/kg$ of exchangeable $K^+$, $13.5cmol^+/kg$ of exchangeable $Ca^{2+}$, $3.3cmol^+/kg$ of exchangeable $Mg^{2+}$.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.