The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.
Sang-Chul Lee;Ki-Ha Hwang;Sang-Hee Kang;Kun-Jai Lee
Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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v.2
no.1
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pp.35-40
/
2004
Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model
Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.
In the past, several experimental investigations aiming at characterizing the natural circulation (NC) behavior in test facilities were carried out. They showed a variety of flow patterns characterized by an inverted U-shape of the NC flow curve versus primary mass inventory. On the other hand, attempts to reproduce such curves using thermal-hydraulic system codes, showed 10-30% differences between the measured and calculated NC mass flow rate. Actually, the used computer codes are generally based upon nodalization using single U-tube representation. Such model may not allow getting accurate simulation of most of the NC phenomena occurring during such tests (like flow redistribution and flow reversal in some SG U-tubes). Simulations based on multi-U-tubes model, showed better agreement with the overall behavior, but remain unable to predict NC phenomena taking place in the steam generator (SG) during the experiment. In the current study, the CATHARE code is considered in order to assess a NC characterization test performed in the four loops PKL facility. For this purpose, four different SG nodalizations including, single and multi-U-tubes, 1D and 3D SG inlet/outlet zones are considered. In general, it is shown that the 1D and 3D models exhibit similar prediction results up to a certain point of the rising part of the inverted U-shape of the NC flow curve. After that, the results bifurcate with, on the one hand, a tendency of the 1D models to over-predict the measured NC mass flow rate and on the other hand, a tendency of the 3D models to under-predict the NC flow rate.
In accordance with a change in the management information system containing the management record and planning for the entire national forest in South Korea by an amendment of the relevant law (The national forest management planning and methods, Korea Forest Service), in this study, average, the maximum, and the minimum values for DBH were presented while only average values were required before the amendment. In this regard, there is a need for an estimation algorithm by which all the existing values for DBH established before the revision can be converted to the highest and the lowest ones. The purpose of this study is to develop an estimation equation to automatically show the minimum and the maximum values for DBH for 12 main tree species from the data in the national forest management information system. In order to develop the estimation equation for the minimum and the maximum values for DBH, there was exploited the 6,858 fixed sample plots of the fifth and the sixth national forest inventory between in 2006 and 2015. Two estimation models were applied for DBH-tree age and DHB-tree height using such growth variables as DBH, tree age, and height, to draw the estimation equation for the maximum and the minimum values for DBH. The findings showed that the most suitable model to estimate the minimum and the maximum values for DBH was Dmin=a+bD+cH, Dmax=a+bD+cH with the variables of DBH and height. Based on these optimal models, the estimation equation was devised for the minimum and the maximum values for DBH for the 12 main tree species.
Chung, Sang Young;Yim, Jong Su;Cho, Hyun Kook;Jeong, Jin Hyun;Kim, Sung Ho;Shin, Man Yong
Journal of Korean Society of Forest Science
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v.98
no.4
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pp.409-416
/
2009
Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.
Korean Journal of Agricultural and Forest Meteorology
/
v.15
no.4
/
pp.219-233
/
2013
We employed the ecological niche modeling framework using GARP (Genetic Algorithm for Ruleset Production) to model the current and future geographic distribution of Pinus densiflora based on environmental predictor variable datasets such as climate data including the RCP 8.5 emission climate change scenario, geographic and topographic characteristics, soil and geological properties, and MODIS enhanced vegetation index (EVI) at 4 $km^2$ resolution. National Forest Inventory (NFI) derived occurrence and abundance records from about 4,000 survey sites across the whole country were used for response variables. The current and future potential geographic distribution of Pinus densiflora, one of the tree species dominating the present Korean forest was modeled and mapped. Future models under RCP 8.5 scenarios for Pinus densiflora suggest large areas predicted under current climate conditions may be contracted by 2090 showing range shifts northward and to higher altitudes. Area Under Curve (AUC) values of the modeled result was 0.67. Overall, the results of this study were successful in showing the current distribution of major tree species and projecting their future changes. However, there are still many possible limitations and uncertainties arising from the select of the presence-absence data and the environmental predictor variables for model input. Nevertheless, ecological niche modeling can be a useful tool for exploring and mapping the potential response of the tree species to climate change. The final models in this study may be used to identify potential distribution of the tree species based on the future climate scenarios, which can help forest managers to decide where to allocate effort in the management of forest ecosystem under climate change in Korea.
Sora Kim;Jongsu Yim;Sunjung Lee;Jungeun Song;Hyelim Lee;Yeongmo Son
Journal of Korean Society of Forest Science
/
v.112
no.2
/
pp.209-216
/
2023
This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m3 . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.
Journal of Korean Society of Industrial and Systems Engineering
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v.39
no.4
/
pp.106-116
/
2016
MTO (Make to Order) is a manufacturing process in which manufacturing starts only after a customer's order is received. Manufacturing after receiving customer's orders means to start a pull-type supply chain operation because manufacturing is performed when demand is confirmed, i.e. being pulled by demand (The opposite business model is to manufacture products for stock MTS (Make to Stock), which is push-type production). There are also BTO (Build to Order) and ATO (Assemble To Order) in which assembly starts according to demand. Lean manufacturing by MTO is very efficient system. Nevertheless, the process industry, generally, which has a high fixed cost burden due to large-scale investment is suitable for mass production of small pieces or 'mass customization' defined recently. The process industry produces large quantities at one time because of the lack of manufacturing flexibility due to long time for model change or job change, and high loss during line-down (shutdown). As a result, it has a lot of inventory and costs are increased. In order to reduce the cost due to the characteristics of the process industry, which has a high fixed cost per hour, it operates a stock production system in which it is made and sold regardless of the order of the customer. Therefore, in a business environment where the external environment changes greatly, the inventory is not sold and it becomes obsolete. As a result, the company's costs increase, profits fall, and it make more difficult to survive in the competition. Based on the customer's order, we have built a new method for order system to meet the characteristics of the process industry by producing it as a high-profitable model. The design elements are designed by deriving the functions to satisfy the Y by collecting the internal and external VOC (voice of customer), and the design elements are verified through the conversion function. And the Y is satisfied through the pilot test verified and supplemented. By operating this make to order system, we have reduced bad inventories, lowered costs, and improved lead time in terms of delivery competitiveness. Make to order system in the process industry is effective for the display glass industry, for example, B and C groups which are non-flagship models, have confirmed that the line is down when there is no order, and A group which is flagship model, have confirmed stock production when there is no order.
In the national greenhouse gas inventory, a settlements category has never been included owing to the lack of activity data. Therefore, this study was conducted to obtain basic data for estimating biomass carbon storage in settlements. Nondestructive stem analysis with a laser dendrometer was performed on four major street tree species (Metasequoia glyptostroboides, Prunus armeniaca, Ginkgo biloba, and Acer buergerianum) in Wonju city, South Korea. Allometric equations of the aboveground volume were developed using five models, and allometric equations of crown area were developed with diameter at breast height (DBH) as an independent variable. The best performing allometric equations were aD2+bD+c for M.glyptostroboides and G. biloba, aD+bD2 for P. armeniaca, and a+bD2 for A. buergerianum. Regarding the allometric equations of crown area with DBH as an independent variable, G. biloba and A. buergerianum exhibited low coefficients of determination (R2), i.e., < 0.364, whereas M. glyptostroboides and P. armeniaca exhibited satisfactory R2 values, i.e., > 0.767, probably due to different street tree management practices. The allometricequations in this study will support the carbon inventory of settlements and urban tree monitoring in management practices.
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