• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.791-801
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• 2010

Forest soil carbon model is a useful tool for understanding complex soil carbon cycle in forests and estimating dynamics of soil carbon to climate change. However, studies on development and application of the model are insufficient in Korea. The need for development of Korean model is now growing, because there are notable problems and limitations for adapting overseas models in Korea to meet the requirements of the international organizations such as IPCC, which demands highly reliable data for national reports. Therefore, we have studied 7 overseas forest soil carbon models (CBM-CFS3, CENTURY, Forest-DNDC, ROMUL, RothC, Sim-CYCLE, YASSO), analyzed and compared their structure, decomposition mechanism, initializing process and, input and output data. Then we evaluated applicability of these models in Korea with three criteria; availability of input data, performance of model, and possibility of regional modification. Finally, a systematic process for applying a new model was suggested based on these analyses.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.7
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• pp.593-598
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• 2006

This study was conducted to investigate the effect of tillage methods such as plowing and rotary tillage (PRT), rotary tillage (RTG), no-tillage after barley straw application (NTB), and barley straw mulching after plowing and rotary tillage (BPR) on the growth and the yield of soybean when cultivated after the cultivation of barley. The methods were compared with the control method in which plowing and rotary tillage after barley straw incineration was applied. Barley straw application resulted in increase in organic matter, total nitrogen, phosphate, and exchangeable cation regardless of tillage methods. Porosity and moisture level in paddy soil was ranked as follows : PRT > RTG > BPR > control > NTB. Decomposition rate of barley straw dramatically increased to 41.7% toward 30 days after soybean sowing, higher in NTB, DRB, and RTG than in BPR. Weed occurrence was decreased 36% in NTB and 40% in BPR. Root activity, nodulation and the dry weight per plant of soybean at flowering stage were highest in NTB and lowest in PRT. Soybean yield in NTB was 3,070 kg/ha increasing 19%, whereas that in PRT was not increased. Therefore in case of a frequent rain during the cultivation of soybean in paddy field PRT could result in excess moisture level in soil, the cultivation without tillage is desirable.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.257-265
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• 2016

In this study, an optimal design for motor-driven track type traveling system applied into high clearance working machineries in orchard is proposed. Tractive performance and hill climbing ability were predicted and evaluated for the optimal motor traveling system by taking into account of soil characteristics in orchard utilizing the high clearance working machineries. Design criteria for tractive performance were based on the traction force calculated from tractive effort subtracted by motion resistance, while hill climbing ability had its design criteria that fulfill the climbing 20% slope ground at a speed of 3km/h. Based on the evaluation results of traction and climbing ability, two DC48V, 4500rpm, 1.6kW AC motors were independently applied to both left and right side of orbits; each motor is designed to transmit power on driving sprocket of track type traveling system via 50:1 reduction gear ratio. The motor-driven track type traveling system developed in the study found to have 396 kgf of tractive force, which is 12.5% higher than climbing resistance at orchard soil having 20% slope ground (352 kgf), demonstrating sufficient tractive performance and hill climbing ability.

• Journal of the Korean GEO-environmental Society
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• v.16 no.12
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• pp.45-52
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• 2015

With the rising interest in the environment, more attention on ecological restoration for damaged slope surface to restore its original state has been drawn. Generally, the most useful method is vegetation based spray work. This method uses green soil including sewage sludge, sawdust, paper sludge, and weathered granite soil. However, because there are neither accurate information nor test values about green soil, green soil is often lost by environmental factors such as rainfalls and strong winds. To solve the problem of green soil, it is necessary to prepare design standards about green soil, and conduct studies to deal with green soil loss in consideration of various variables including basic material property, soil quality of slope surface, and weather. This study was conducted in the mixture of green soil and eco-friendly soil stabilizer. With green soil, basic material property test and compaction test were conducted for the analysis on the basic characteristics of green soil. In the mixture with soil stabilizer at a certain ratio, we conducted shear strength test depending on the ratio in order to analyze the maximum shear strength, cohesion and the change in internal friction angles. Furthermore, in the mixture ratio of green soil and soil stabilizer, which is the same as the ratio in the shear strength test, an inclination of slope surface was made in laboratory for the analysis on erosion and germination rate. Finally, this study evaluated the most effective and economic mixing ratio of soil stabilizer to cope with neighboring environmental factors. According to the test, the shear strength of green soil increased up to 51% rely onto the mixing ratio of and a curing period, and its cohesion and internal friction angle also gradually increases. It is judged that the mixture of soil stabilizer was effective in improving shear strength and thereby increased the stability of green soil.

• Journal of Soil and Groundwater Environment
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• v.12 no.2
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• pp.37-46
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• 2007

This study developed composition-plastic media with woodchips and plastic as main materials, and examined the performance of media. Compared to the existing commercial media, the media had similar performance in removal efficiency and microbes attaching characteristic, and was evaluated that they are distinguished from economic side. Performance test of media was conducted to examine the removal capacity of toluene and hydrogen sulfide in a gas stream by using a lab-scale biotrickling filter systems packed with them. At a volumetric loading of $1.5\;m^3/hr$ with inlet concentration 260 ppm and empty bed residence time (EBRT) 42s, the toluene removal efficiency was shown over 90%, and the maximum elimination capacity of toluene in the biotrickling filter was $77g/m^3{\cdot}hr$. Effective co-treatments of $H_2S$ and Toluene were observed in the lab-scale biotrickling filters. The maximum elimination capacity of $H_2S$ was $100\;g-S/m^3{\cdot}hr$. Up to 100 ppm, the concentration of $H_2S$ did not have an effect on toluene removal efficiency, but the removal efficiency of toluene decreased with increasing inlet $H_2S$ concentration.

• Journal of Soil and Groundwater Environment
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• v.10 no.1
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• pp.1-5
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• 2005

Nitrate reduction with zero valent iron $(Fe^0)$ has been extensively studied, but the proper treatment for ammonium byproduct has not been reported yet. In groundwater, however, ammonium is regarded as contaminant species, and particularly, its acceptable level is regulated to 0.5 mg-N/L. for drinking water. This study is focused on developing new material to reduce nitrate and properly remove ammonium by-products. A new material, Fe-loaded zeolite, is derived from zeolite modified by Fe(II) chloride followed by reduction with sodium borohydride. Batch experiments were performed without buffer at two different pH to evaluate the removal efficiency of Fe-loaded zeolite. After 80 hr reaction time, Fe loaded zeolite showed about $60\%$ nitrate removal at initial pH of 3.3 and $40\%$ at pH of 6 with no ammonium release. Although iron filing showed higher removal efficiency than Fe-loaded zeolite at each pH, it released a considerable amount of ammonium stoichiometrically equivalent to that of reduced nitrate. In terms of nitrogen species including $NO_3-N$ and $NH_4^+-N$, Fe-loaded zeolite removed about $60\%\;and\;40\%$ of nitrogen in residual solution at initial pH of 3.3 and 6, respectively, while the removal efficiency of iron filing was negligible.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.365-373
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• 2017

This study proposes a newly developed dry washing method for removing pollutants such as total petroleum hydrocarbon (TPH) and oxidized iron from the surface of ballast gravel. A batch-type dry washing method showed a good performance in a previous study. In this study, a continuous-type dry washing system, instead of a batch-type system, was prepared to improve the efficiency of the system. A drier and a separator were also applied to this system as pre-treatment process, and the performance of this system was evaluated. In this experiment, blasting media was blasted on the polluted gravels through 12 nozzles by a pressure of $5-6kg/cm^2$ for 20-30 mins to remove TPH and oxidized iron. It was found to be possible to remove 80-90% of TPH and oxidized iron by using this system. Several ways to improve the performance were suggested in this study.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.123-141
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• 2023

A sufficient amount of data with quality is needed for training artificial neural networks (ANNs). However, developing ANN models with a small amount of data often appears in engineering fields. This paper presented an ANN model to improve prediction performance of the ammonia emission amount with 83 data. The ammonia emission rate included eleven inputs and two outputs (maximum ammonia loss, N_max and time to reach half of N_max, K_m). Categorical input variables were transformed into multi-dimensional equal-distance variables, and 13 data were added into 66 training data using a generative adversarial network. Hyperparameters (number of layers, number of neurons, and activation function) of ANN were optimized using Gaussian process. Using 17 test data, the previous ANN model (Lim et al., 2007) showed the mean absolute error (MAE) of K_m and N_max to 0.0668 and 0.1860, respectively. The present ANN outperformed the previous model, reducing MAE by 38% and 56%.