• Title/Summary/Keyword: soil classification

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A Study on Correlations between Compaction Properties and Soil Constants of Granite Soil in the Kangwon Province (강원도에 분포하는 화강토의 다짐특성 및 토질정수의 상관관계에 관한 연구)

  • Yoo, Nam-Jae;Park, Byung-Soo;Hong, Young-Gil
    • Journal of Industrial Technology
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    • v.18
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    • pp.77-86
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    • 1998
  • This study is to provide the useful data for the design and construction of the geotechnical engineering works by collecting and analyzing the soil properties of granite soil in the Kangwon Province. Data base was obtained from 92 field sites in the Kangwon province divided into 15 areas based on administration district. Total numbers of data were 478. Correlations between soil constants, especially compaction properties, were obtained by performing statistical analysis. Analyzed results were as follows. 1. Most of granite soil consists of SM and GM based on United Soil Classification System. 2. Mean gravity of granite soil is 2.65 3. High correlations between optimum moisture content and the maximum dry density, plasticity index and liquid limit are obtained. 4. Analyzed results between other soil constants show relatively low correlation. However, they show consistent trends matchable to geotechnical engineering senses.

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Development of a Soil Moisture Estimation Model Using Artificial Neural Networks and Classification and Regression Tree(CART) (의사결정나무 분류와 인공신경망을 이용한 토양수분 산정모형 개발)

  • Kim, Gwangseob;Park, Jung-A
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.31 no.2B
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    • pp.155-163
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    • 2011
  • In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

Spectral Reflectance of Soils Related to the Interaction of Soil Moisture and Soil Color Using Remote Sensing Technology (RS 기법을 이용한 토양수분과 토양 색에 관련된 토양의 분광반사)

  • 박종화
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.45 no.5
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    • pp.77-84
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    • 2003
  • Recent advances in remote sensing techniques provide the potential for monitoring soil color as well as soil moisture conditions at the spatial and temporal scales required for detailed local modeling efforts. Soil moisture as well as soil color is a key feature used in the identification and classification of soils. Soil spectral reflectance has a direct relationship with soil color, as well as to other parameters such as soil moisture, soil texture. and organic matter. We evaluate the influence of seven soil properties, soil color and soil moisture, on soil spectral reflectance. This paper presents the results obtained from the ground-truth spectral reflectance measurements in the 300-1100 nm wavelength range for various land surfaces. The results suggest that the reflectance properties of soils are related to soil color, soil texture, and soil moisture. Increasing soil moisture content generally decreases soil reflectance which leads to parallel curves of soil reflectance spectra across the entire shortwave spectrum. We discuss the relationships between the soil reflectance and the Munsell Soil Color Charts which contain standard color chips with colors specified by designations for hue, value, and chroma.

Topographic Characteristics, Formation and Classification of Soils Developed in Limestone -II. Clay Mineralogical Properties, Formation and Classification of Limestone Soils from Yeongweal Area of Gangweon-Do (석회암(石灰岩) 토양(土壤)의 지형적(地形的) 특성(特性)과 생성(生成) 및 분류(分類) -II. 강원도(江原道) 영월지역(靈越地域) 석회암(石灰岩) 토양(土壤)의 점토광물특성(粘土鑛物特性)과 생성(生成) 및 분류(分類))

  • Jung, Sug-Jae;Kim, Tai-Soon;Kim, Young-Ho;Moon, Joon
    • Korean Journal of Soil Science and Fertilizer
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    • v.23 no.1
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    • pp.1-7
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    • 1990
  • Clay mineralogical properties, soil formation and classification for the limestone derived soil were discussed from the 4 soil series, Pyeongchang, Anmi, Yulgog, and Mungyeong, distributed over the area of Bangjeol-Ri, Yeongweol-Eup, Gangweon-Do. The results are as follows; 1. Kaolinite and Al-interlayered vermiculite were the main clay minerals and other minerals were illite, vermiculite, and chlorite. 2. CEC of topsoil ranged from 10.5 to 22.9me/100g and that of sub-soil ranged from 10.0 to 23.8me/100g. CEC seemed to be increased with descending from topograpical position and had slightly significant positive correlation. 3. Lithologic changes as a runction of soil depth showed smooth change for Pyeongchang, discontinuity for Anmi, unpredictability for Yulgog, and uniformity for Mungyeong series. 4. According to the new system of soil taxanomy Pyeongchang series was classified as Typic Hapludalfs, Anmi as Fluventic Eutrochrepts, Yulgog as Fluvaquentic Eutrochrepts, and Mungyeong as Aeric Haplaquepts.

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Construction of Correlation between Basic Soil Properties and Deformation Modulus of Trackbed Soils Based on Laboratory and Field Mechanical Tests (역학적 실내외 시험에 의한 철도궤도 상부노반용 흙재료의 기본물성과 변형계수 상관성 평가)

  • Park, Jae Beom;Choi, Chan Yong;Ji, Sang Hyun;Lim, Sang Jin;Lim, Yu Jin
    • Journal of the Korean Society for Railway
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    • v.19 no.2
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    • pp.204-212
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    • 2016
  • The soils used as trackbed in Korea are selected using USCS utilizing basic soil properties such as Grain Size Distribution(GSD), % passing of #200 sieve ($P_{200}$), % passing of #4 sieve ($P_4$), Coefficient of uniformity ($C_u$), and Coefficient of curvature ($C_c$). Degree of compaction of the soils adapted in the code by KR should be evaluated by maximum dry density (${\gamma}_{d-max}$) and deformation modulus $E_{v2}$. The most important influencing factor that is critical to stability and deformation of the compacted soils used as trackbed is stiffness. Thus, it is necessary to construct a correlation between the modulus and the basic soil properties of trackbed soil in order to redefine a new soil classification system adaptable only to railway construction. To construct the relationship, basic soil test data is collected as a database, including GSD, maximum dry unit weight (${\gamma}_{d-max}$), OMC, $P_{200}$, $P_4$, $C_u$, $C_c$, etc.; deformation modulus $E_{v2}$ and $E_{vd}$ are obtained independently by performing a Repeated Plated Bearing Test (RPBT) and Light Weight Deflectometer Test (LWDT) for ten different railway construction sites. A linear regression analysis is performed using SPSS to obtain the relationship between the basic soil properties and the deformation modulus $E_{v2}$ and $E_v$. Based on the constructed relationship and the various obtained mechanical test data, a new soil classification system will be proposed later as a guideline for the design and construction of trackbed foundation in Korea.

Classification of Remote Sensing Data using Random Selection of Training Data and Multiple Classifiers (훈련 자료의 임의 선택과 다중 분류자를 이용한 원격탐사 자료의 분류)

  • Park, No-Wook;Yoo, Hee Young;Kim, Yihyun;Hong, Suk-Young
    • Korean Journal of Remote Sensing
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    • v.28 no.5
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    • pp.489-499
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    • 2012
  • In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

A Performance Comparison of Machine Learning Classification Methods for Soil Creep Susceptibility Assessment (땅밀림 위험지 평가를 위한 기계학습 분류모델 비교)

  • Lee, Jeman;Seo, Jung Il;Lee, Jin-Ho;Im, Sangjun
    • Journal of Korean Society of Forest Science
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    • v.110 no.4
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    • pp.610-621
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    • 2021
  • The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

Characteristics and classification of paddy soils on the Gimje-Mangyeong plains (김제만경평야(金堤萬頃平野)의 답토양특성(沓土壤特性)과 그 분류(分類)에 관(關)한 연구(硏究))

  • Shin, Yong Hwa
    • Korean Journal of Soil Science and Fertilizer
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    • v.5 no.2
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    • pp.1-38
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    • 1972
  • This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) = $$-0.2491x^2+6.0388x-1.1251$$ y(subsoil or subsurface soil, clay content) = $$-0.31646x^2+7.84818x-2.50008$$ y(surface soil, organic carbon content) = $$-0.0089x^2+0.2192x+0.1366$$ y(subsoil or subsurface soil, pH) = $$-0.0178x^2-0.04534x+8.3531$$ Soil profile development, soil color, depositional and organic layers, soil texture and soil reaction etc. are thought to be the major items that should be considered in a paddy soil classification. It was found that most of the soils belonging to the moderately well, somewhat poorly and poorly drained fine and medium textured soils and moderately deep fine textured soils over coarse materials, produce higher paddy yields in excess of 3,750 kg/ha. and most of the soils belonging to the coarse textured soils, well drained fine textured soils, moderately deep medium textured soils over coarse materials and saline soils, produce yields less than 3,750kg/ha. Soil texture of the profile, available soil depth, salinity and gleying of the surface and subsurface soils etc. seem to be the major factors determining rice yields, and these factors are considered when establishing suitability groups for paddy land. The great group, group, subgroup, family and series are proposed for the classification categories of paddy soils. The soil series is the basic category of the classification. The argillic horizon (Bt horizon) and cambic horizon (B horizon) are proposed as two diagnostic horizons of great group level for the determination of the morphological properties of soils in the classification. The specific soil characteristics considered in the group and subgroup levels are soil color of the profile (bluish gray, gray or yellowish brown), salinity (salic), depositonal (fluvic) and muck layers (mucky), and gleying of surface and subsurface soils (gleyic). The family levels are classified on the basis of soil reaction, soil texture and gravel content of the profile. The definitions are given on each classification category, diagnostic horizons and specific soil characteristics respectively. The soils on these plains are classified in eight subgroups and examined under the existing classification system. Further, the suitability group, can be divided into two major categories, suitability class and subclass. The soils within a suitability class are similar in potential productivity and limitation on use and management. Class 1 through 4 are distinguished from each other by combination of soil characteristics. Subclasses are divided from classes that have the same kind of dominant limitations such as slope(e), wettness(w), sandy(s), gravels(g), salinity(t) and non-gleying of the surface and subsurface soils(n). The above suitability classes and subclasses are examined, and the definitions are given. Seven subclasses are found on these plains for paddy soils. The classification and suitability group of 15 paddy soil series on the Gimje-Mangyeong plains may now be tabulated as follows.

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Assessment of Soil Washing Efficiency for Arsenic Contaminated Site Adjacent to Jang Hang Refinery (장항제련소 주변 비소오염토양의 특성분석에 따른 토양세척 처리효율 평가)

  • Moon, So-Young;Oh, Min-Ah;Jung, Jun-Kyo;Choi, Sang-Il;Lee, Jai-Young
    • Journal of Soil and Groundwater Environment
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    • v.16 no.1
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    • pp.71-81
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    • 2011
  • Cause of contamination in the study area nearby Jang Hang Refinery is dust scattering in refinery stack, and soil washing treatment is one of the proper technologies for soil remediation in this area. Site conditions frequently limit the selection of a treatment process. A treatment technology may be eliminated based on the soil classification or physicochemical characteristics of soil. This study was assessed the soil washing efficiency by conducting of soil characteristic analysis in the vicinity of Jang Hang Refinery Stack within a 2 km radius. Also, it was decided about remedial range with comparative analysis of As in soil by Korean Standard Test Method before/after revision, whereupon As concentration in soil showed a increasing tendency after revision. As a result, the soil washing using the size separation of soil was determined through identifying of As species in the soil. In this site, only particle size distribution and water content of soil can provide the initial means of screening for the potential use of soil washing.

Studies on the Interpretative Classification of Paddy Soils in Korea I : A Study on the Classification of Sandy Paddy Soils (우리나라 답토양(畓土壌)의 실용적분류(実用的分類)에 관(関)한 연구(硏究) -제1보(第一報) 사질답(砂質畓) 분류(分類)에 관(関)하여)

  • Jung, Yeun-Tae;Yang, Euy-Seog;Park, Rae-Kyung
    • Korean Journal of Soil Science and Fertilizer
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    • v.15 no.2
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    • pp.128-140
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    • 1982
  • The distribution and practical classification of sandy paddy soils, which have the most extensive acreage among low productive paddy soils in Korea and have distinctive improvement effects, were studied to propose a tentative new classification system of sandy textured paddy soils as a means of improving the "Paddy Soil Type Classification" scheme used. The results are summarized as follows; 1. The potential productivity of sandy textured paddy soils was about 86% of normal paddy and the coefficient of variation was relatively high indicating that the properties of soils included were not sufficiently homogeneous. 2. As the poorly drained and halomorphic (> 16 mmhos/cm of E.C. at $25^{\circ}C$) sandy soils are not included in the "Sandy Soil" type according to the criteria of "Soil Type Classification", the recommendation of "adding clay earth" become complicated, and the soil type have to change when the salts washed away or due to ground water table fluctuations. 3. Coarse textured soils were entirely included in the "Sandy Soils" in the tentative criteria of sandy soil classification proposed, and the sandy soils were subdivided into 4 subtypes that is "Oxidized leaching sandy paddy", Red-ox. intergrading sandy paddy", "Reduced accumulating sandy paddy" and "Reduced halomorphic sandy paddy". The system of sandy soil classification proposed were consisted of following categories; Type (Sandy paddy)-Sub-type (4)-Texture family (5)-Soil series (48). 4. The variation of productivities according to the proposed scheme was more homogenized than that of the present device. 5. The total extent of sandy paddy soils was 409, 902 ha (32.3% of total paddy) according to the present classification system, but the extent reached 492,983 ha (38.9%) by the proposed system. The provinces of Gyeong-gi (88.923ha), Jeon-bug (69.717 ha), Gyeong-bug (55.390 ha) have extensive acreage of sandy paddy soils, and the provinces that had high ratio of sandy paddy soils were Gang-weon (58.9%), Gyeong-gi (50.5%), Chung-bug (48.5%), Jeon-bug (41.0%) etc. The ratio was increased by the proposed scheme, e.g. 71.4% in the case of Gang-weon prov. 6. According to the suitability group of paddy soils, the sandy soils mostly belong to 3 class (69.1%) and 4 class (29.2%). Coarse loamy textural family (59.2%) and coarse silty (16.1 %) soils were dominantly distributed. 7. The "Red-ox. intergrading subtype" of sandy paddy pertinent to 49.6% (245,012 ha) while the "Oxidized leaching sub-type" reaches to 33.5% (64,890 ha) and the remained 16.9% (83,081ha) belong to "Reduced accumulating sub-type (14.0%) and "Reduced halomorphic sub-type (2.9%)" according to the proposed scheme.

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