• Korean Journal of Soil Science and Fertilizer
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• v.39 no.6
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• pp.334-338
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• 2006

This study was conducted to evaluate the effect of improvement of soil physical properties such as deep plowing, subsoil breaking and diagnostic fertilization on the yield of rice and nitrogen-use efficiency in direct seeding on flooded paddy surface of rice. The effects of deep plowing, subsoil breaking and diagnostic application of N, P, K fertilizers, Latex coated urea(LCU), compost, silicate were investigated. The soil physical properties, such as bulk density, hardness and porosity were improved and the content of organic matter and available $SiO_2$ were also increased by deep plowing and subsoil breaking. The amount of $NH_4-N$ in soil was highly increased by diagnostic fertilization and deep plowing at 5th leaf stage. The nitrogen-use efficiency was the highest at the diagnostic application of LCU 70% applied as basal dressing with subsoil breaking. The yield of rice increased by 8% under the diagnostic application of LCU 70% applied as basal dressing with subsoil breaking compared with the conventional application.

• Journal of the Geological Society of Korea
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• v.54 no.5
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• pp.545-556
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• 2018

This study was conducted to estimate the deep percolation using numerical modeling and field observation data based on rainfall in Haean basin. Soil moisture sensors were installed to monitoring at 30, 60 and 90 cm depths in four sites (YHS1-4) and automatic weather station was installed to around YHS3. Soil moisture and meteorological data was observed from March 25, 2017 to March 25, 2018 and May 06, 2016 to May 06, 2018, respectively. Numerical analysis was performed from June to August, 2017 using the HYDRUS-1D. Average soil moisture contents were high to generally in YHS3 for 0.300 to $0.334m^3/m^3$ and lowest in YHS1 for 0.129 to $0.265m^3/m^3$ during the soil moisture monitoring period. The results of soil moisture flow modeling showed that field observations and modeling values were similar but the peak values were larger in the modeling result. Correlation analysis between observation and modeling data showed that r, $r^2$ and RMSE were 0.88, 0.77, and 0.0096, respectively. This show high correlation and low error rate. The total deep percolation was 744.2 mm during the period of modelling at 500 cm depth. This showed that 61.3% of the precipitation amount (1,214 mm) was recharged in 2017. Deep percolation amount was high in the study area. This study is expected to provide basic data for the estimation of groundwater recharge through unsaturated zone.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Geotechnical Engineering
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• v.9 no.1
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• pp.7-20
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• 1993

The first Korean earthquake resistant design code was enacted in 1988. In the code, the soil factor which takes into account both the soil amplification factor and the soil -structare interaction effect is divided into three groups : soil factor, 5 : 1.0, 1.2 and 1.5. In order to assist in choosing the soil factors appropriately in the earthquake resistant design, the local site effects on the based shear force induced by earthquakes are considered in depth for typical soil conditions in Korea. The depth of the alluvial and/or weathered zone is usually not deep and the fresh rock is found at depth shallower than 20 meters, and even at about 10 meters around Seoul. One dimensional wave propagation theory and the elastic half space method are used to obtain the soil -structure interaction effect as well as the soil amplification effect. The kinematic interaction effect due to scattering of waves by pile foundation is also considered. Finally, the soil factor is recommended for each soil condition from loose state to dense, and also from shallow soil depth to deep, so that the designer can choose the factor with-out difficulty.

• Journal of Soil and Groundwater Environment
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• v.25 no.3
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• pp.74-83
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• 2020

Since fine dust (PM₁₀) has a significant influence on soil and groundwater composition during dry and wet deposition processes, it is of a vital importance to understand the fate and transport of aerosol in geological environments. Fine dust is formed after the chemical reaction of several precursors, typically observed in short intervals within a few hours. In this study, deep learning approach was applied to predict the fate of fine dust in an urban area. Deep learning training was performed by combining convolutional neural network (CNN) and recurrent neural network (RNN) techniques. The PM₁₀ concentration after 1 hour was predicted based on three-hour data by setting SO₂, CO, O₃, NO₂, and PM₁₀ as training data. The obtained coefficient of determination value, R², was 0.8973 between predicted and measured values for the entire concentration range of PM₁₀, suggesting deep learning method can be developed into a reliable and viable tool for prediction of fine dust concentration.

• Water for future
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• v.10 no.2
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• pp.81-90
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• 1977

The research is conducted to study the effect of the soil physical properties in the direct sowing culture on the water requirement, growth, and yield of rice with Early-Tongil at the experimental paddy field of the Sangju agri. and seri. junior college in Keyngbuk province from 6th May to 15th September in 1977. The experimental plots are designed with the four plots which are non-irrigated standard (plowing to 15cm), non-irrigated deep lowed (plowing to 25cm), irrigated standard (plowing to 15cm), and irrigated deep plowing plot (plowing to 25cm) and also each plot is repreated four times by the split plot design. The results obtained are summarized as follows: 1) The soil sample was ML to 10cm depth from ground surface and those from 10cm to 20cm depth and from 20cm to 30cm were CL. Each specific gravity was 2. 6, 2. 6 and 2. 7. 2) The weather during culturing period was the sane as the normal year of mean temperature. The precipitation was little and the distribution of it was disordered comparing to normal year but the heavy sunshine gave good effect on ripening. 3) Percolation loss was increased more at the non-irrigated plot than at the irrigated plot, and that of deep-plowed plot was increased more. 4) Grain yield per 10a. of non-irrigated deep plowed plot was 898kg, it was greated than others but there wa no significance. 5) A significant difference in the number of spikelets per panicle was found between nonirrigated plot and irrigated plot, and the number of spiklelets per panicle at the nonirrigated plot was more than that of the irrigated plot. But there was no significance in the other yield components-number of panicle, fertility abd ripening ratio-at the irrigated plot, ut weight of 100 grains was higher at non-irrigated plot. 6) Yield and growth at the deep plowed plot were higher than those of standard plowed plot.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.15-28
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• 2023

The compaction properties of the soil change depending on the physical properties, and are also affected by crushing of the particles. Since the particle size distribution of soil affects the engineering properties of the soil, it is necessary to analyze the material properties to understand the compaction characteristics. In this study, the size of each sieve was classified into four in the particle size analysis as a material property, and the compaction characteristics were evaluated by multiple regression and maximum dry unit weight. As a result of maximum dry unit weight prediction, multiple regression analysis showed R² of 0.70 or more, and DNN analysis showed R² of 0.80 or more. The reliability of the prediction result analyzed by DNN was evaluated higher than that of multiple regression, and the analysis result of DNN-T showed improved prediction results by 1.87% than DNN. The prediction of maximum dry unit weight using particle size distribution seems to be applied to evaluate the compacting state by identifying the material characteristics of roads and embankments. In addition, the particle size distribution can be used as a parameter for predicting maximum dry unit weight, and it is expected to be of great help in terms of time and cost of applying it to the compaction state evaluation.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.4
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• pp.341-346
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• 2016

This study was conducted to analyze soil physico-chemical properties of agricultural land composed from the river-bed sediments. We investigated the changes of soil physico-chemical properties at 30 different sampling sites containing paddy, upland and plastic film house from 2012 to 2015. pH, exchangeable calcium and magnesium decreased gradually in paddy soils during the four years, whereas the available $P_2O_5$, exchangeable Ca, Mg and EC increased in upland and plastic film house soil. For the soil physical properties, bulk density and hardness of topsoil were $1.47g\;cm^{-3}$ and 21.5 mm and those of subsoil were $1.71g\;cm^{-3}$ and 25.7 mm in paddy soils. In upland soils, bulk density and hardness of topsoil were $1.48g\;cm^{-3}$ and 15.9 mm and those of subsoil were $1.55g\;cm^{-3}$ and 16.9 mm. In plastic film house soils, bulk density and hardness of topsoil were $1.42g\;cm^{-3}$ and 14.4 mm and those of subsoil were $1.40g\;cm^{-3}$ and 18.5 mm, respectively. The penetration hardness was higher than 3 MPa below soil depth 20 cm, and it is impossible to measure below soil depth 50 cm. As these results, in agricultural anthropogenic soils dredged from river basins, the pH, amount of organic matter and exchangeable cations decreased and soil physical properties also deteriorated with time. Therefore, it is needed to apply more organic matters and suitable amount of fertilizer and improve the soil physical properties by cultivating green manure crops, deep tillage, and reversal of deep soils.

• Korean Journal of Environmental Agriculture
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• v.41 no.4
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• pp.230-235
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• 2022

BACKGROUND: Ammonia gas emitted from nitrogen fertilizers applied in agricultural land is an environmental pollutant that catalyzes the formation of fine particulate matter (PM2.5). A significant portion (12-18%) of nitrogen fertilizer input for crop cultivation is emitted to the atmosphere as ammonia gas, a loss form of nitrogen fertilizer in agricultural land. The widely practiced method for fertilizer use in agricultural fields involves spraying the fertilizers on the surface of farmlands and mixing those with the soils through such means as rotary work. To test the potential reduction of ammonia emission by nitrogen fertilizers from the soil surface, we have added N, P, and K at 2 g each to the glass greenhouse soil, and the ammonia emission was analyzed. METHODS AND RESULTS: The treatment consisted of non-fertilization, surface spray (conventional fertilization), and soil depth spray at 10, 15, 20, 25, and 30 cm. Ammonia was collected using a self-manufactured vertical wind tunnel chamber, and it was quantified by the indophenol-blue method. As a result of analyzing ammonia emission after fertilizer treatments by soil depth, ammonia was emitted by the surface spray treatment immediately after spraying the fertilizer in the paddy soil, with no ammonia emission occurring at a soil depth of 10 cm to 30 cm. In the upland soil, ammonia was emitted by the surface spray treatment after 2 days of treatment, and there was no ammonia emission at a soil depth of 15 cm to 30 cm. Lettuce and Chinese cabbage treated with fertilizer at depths of 20 cm and 30 cm showed increases of fresh weight and nutrient and potassium contents. CONCLUSION(S): In conclusion, rather than the current fertilization method of spraying and mixing the fertilizers on the soil surface, deep placement of the nitrogen fertilizer in the soil at 10 cm or more in paddy fields and 15 cm or more in upland fields was considered as a better fertilization method to reduce ammonia emission.

• Geomechanics and Engineering
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• v.11 no.3
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• pp.361-372
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• 2016

The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.