An innovative approach for visualization of subsurface soil properties

2004 ◽  
Vol 84 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Z. Hu ◽  
B. Bass ◽  
C. W. Chan ◽  
G. H. Huang
Keyword(s):  
Soil Type ◽  
Concentration Distribution ◽  
Soil Layer ◽  
Soil Classification ◽  
Soil Sampling ◽  
Contaminated Site ◽  
Distribution Data ◽  
Contaminant Concentration ◽  
Type Distribution ◽  
Site Characteristics

Subsurface characterization is an important requirement in the decision-making process of selecting a remediation technique for petroleum-contaminated sites. The soil type distribution is one of the most important site characteristics, because it affects selection of the site remediation technique. The visualization of soil type distribution and also the contaminant concentration distribution in the subsurface can help the decision-maker understand the site and select the proper remediation technique. In this paper, we describe the software Soil-Visual (1.0, 1.1), which is used for visualizing the soil sampling data, the soil type distribution, and contaminant concentration distribution of a contaminated site. This software has two functions: (1) to determine the soil particle size distribution and contaminant concentration distribution of the entire site from limited soil sampling data; and (2) to visualize the multi-dimensional soil type distribution and contaminant concentration distribution data of each soil layer on a two-dimensional map. The red-green-blue (RGB) color illustration method has been used in this software to convert the multi-dimensional soil sampling data into a bitmap. Key words: RGB bitmap, soil classification, visualization

scholarly journals Mineralogy of Gypsiferous Soil and the Effect of a dditiveLime Stone waste powder on its Physical Properties

2020 ◽  
pp. 83-91
Author(s):  
Thalar Othman Rashid ◽  
Nadhmia Najmaddin Majeed
Keyword(s):  
Grain Size ◽  
Specific Gravity ◽  
Soil Type ◽  
Soil Classification ◽  
Limestone Powder ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Atterberg Limit ◽  
Gypsum Content ◽  
Physical Testing

The presence of gypsum in soil as bonding agent alters its behavior with a large influence on itsphysical properties.Soil samples were taken from two locations of different gypsum content(S1 = 30.5% and S2= 20%) inMakhmur area. TheUnified soil classification system indicated that soil type was clay with low plasticity(CL). Basic methods of physical testing of soils, such as grain size analysis,specific gravity and atterberg limit were applied. Stabilizationof the gypsiferous soil was performed by addinglimestone waste powder takenfrom Said sadiqandPirmam areas,with different percentages(5%, 15%,25%).The results show that the addition of limestone powder to the tested soils decreases their liquid and plastic limits.

scholarly journals Response Site Analyses of 3D Homogeneous Soil Models

2018 ◽  
Vol 2 (5) ◽  
pp. 238 ◽  
Author(s):  
Davide Forcellini ◽  
Marco Tanganelli ◽  
Stefania Viti
Keyword(s):  
Soil Type ◽  
Soil Depth ◽  
Site Response ◽  
Soil Layer ◽  
Shear Velocity ◽  
Seismic Intensity ◽  
Eurocode 8 ◽  
Seismic Input ◽  
Homogeneous Soil ◽  
Response Site

The seismic excitation at the surface can be determined through Site Response Analyses (SRA) as to account for the specific soil properties of the site. However, the obtained results are largely affected by the model choice and setting, and by the depth of the considered soil layer. This paper proposes a refined 3D analytical approach, by the application of OPENSEES platform. A preliminary analysis has been performed to check the model adequacy as regards the mesh geometry and the boundary conditions. After the model setting, a SRA has been performed on various soil profiles, differing for the shear velocity and representing the different soil classes as proposed by the Eurocode 8 (EC8). Three levels of seismic hazard have been considered. The seismic input at the bedrock has been represented consequently, through as much ensembles of seven ground motions each, spectrum-compatible to the elastic spectra provided by EC8 for the soil-type A (bedrock). Special attention has been paid to the role of the considered soil depth on the evaluation of the surface seismic input. Different values of depth have been considered for each soil type and seismic intensity, in order to check its effect on the obtained results.

scholarly journals Clay-illuvial soils in the Polish and international soil classifications

2015 ◽  
Vol 66 (4) ◽  
pp. 204-213 ◽  
Author(s):  
Cezary Kabała ◽  
Elżbieta Musztyfaga
Keyword(s):  
Environmental Conditions ◽  
Soil Type ◽  
Soil Classification ◽  
The Other ◽  
Soil Types ◽  
Diagnostic Features ◽  
Hierarchical Order ◽  
History Of

AbstractSoil with a clay-illuvial subsurface horizon are the most widespread soil type in Poland and significantly differ in morphology and properties developed under variable environmental conditions. Despite the long history of investigations, the rules of classification and cartography of clay-illuvial soils have been permanently discussed and modified. The distinction of clay-illuvial soils into three soil types, introduced to the Polish soil classification in 2011, has been criticized as excessively extended, non-coherent with the other parts and rules of the classification, hard to introduce in soil cartography and poorly correlated with the international soil classifications. One type of clay-illuvial soils (“gleby płowe”) was justified and recommended to reintroduce in soil classification in Poland, as well as 10 soil subtypes listed in a hierarchical order. The subtypes may be combined if the soil has diagnostic features of more than one soil subtypes. Clear rules of soil name generalization (reduction of subtype number for one soil) were suggested for soil cartography on various scales. One of the most important among the distinguished soil sub-types are the “eroded” or “truncated” clay-illuvial soils.

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

scholarly journals Polish Soil Classification, 6th edition – principles, classification scheme and correlations

2019 ◽  
Vol 70 (2) ◽  
pp. 71-97 ◽  
Author(s):  
Cezary Kabała ◽  
Przemysław Charzyński ◽  
Jacek Chodorowski ◽  
Marek Drewnik ◽  
Bartłomiej Glina ◽  
...  
Keyword(s):  
Soil Type ◽  
Classification Scheme ◽  
Soil Classification ◽  
Parent Material ◽  
Soil Taxonomy ◽  
Soil Science Society ◽  
Sixth Edition ◽  
Living Organisms ◽  
Diagnostic Horizons ◽  
Scientific System

Abstract The sixth edition of the Polish Soil Classification (SGP6) aims to maintain soil classification in Poland as a modern scientific system that reflects current scientific knowledge, understanding of soil functions and the practical requirements of society. SGP6 continues the tradition of previous editions elaborated upon by the Soil Science Society of Poland in consistent application of quantitatively characterized diagnostic horizons, properties and materials; however, clearly referring to soil genesis. The present need to involve and name the soils created or naturally developed under increasing human impact has led to modernization of the soil definition. Thus, in SGP6, soil is defined as the surface part of the lithosphere or the accumulation of mineral and organic materials permanently connected to the lithosphere (through buildings or permanent constructions), coming from weathering or accumulation processes, originated naturally or anthropogenically, subject to transformation under the influence of soil-forming factors, and able to supply living organisms with water and nutrients. SGP6 distinguishes three hierarchical categories: soil order (nine in total), soil type (basic classification unit; 30 in total) and soil subtype (183 units derived from 62 unique definitions; listed hierarchically, separately in each soil type), supplemented by three non-hierarchical categories: soil variety (additional pedogenic or lithogenic features), soil genus (lithology/parent material) and soil species (soil texture). Non-hierarchical units have universal definitions that allow their application in various orders/types, if all defined requirements are met. The paper explains the principles, classification scheme and rules of SGP6, including the key to soil orders and types, explaining the relationships between diagnostic horizons, materials and properties distinguished in SGP6 and in the recent edition of WRB system as well as discussing the correlation of classification units between SGP6, WRB and Soil Taxonomy.

scholarly journals Data Mining Classification Algorithms for Analyzing Soil Data

2021 ◽  
pp. 17-28
Author(s):  
Kazheen Ismael Taher ◽  
Adnan Mohsin Abdulazeez ◽  
Dilovan Asaad Zebari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Soil Type ◽  
Soil Classification ◽  
Well Being ◽  
Experimental Result ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Climate Regulation ◽  
Learning Classifier

Rapid changes are occurring in our global ecosystem, and stresses on human well-being, such as climate regulation and food production, are increasing, soil is a critical component of agriculture. The project aims to use Data Mining (DM) classification techniques to predict soil data. Analysis DM classification strategies such as k-Nearest-Neighbors (k-NN), Random-Forest (RF), Decision-Tree (DT) and Naïve-Bayes (NB) are used to predict soil type. These classifier algorithms are used to extract information from soil data. The main purpose of using these classifiers is to find the optimal machine learning classifier in the soil classification. in this paper we are applying some algorithms of DM and machine learning on the data set that we collected by using Weka program, then we compare the experimental result with other papers that worked like our work.  According to the experimental results, the highest accuracy is k-NN has of 84 % when compared to the NB (69.23%), DT and RF (53.85 %). As a result, it outperforms the other classifiers. The findings imply that k-NN could be useful for accurate soil type classification in the agricultural domain.

Establishment and Validation of Mainline Driver Type Model at Expressway-Ramp Merging Area

2013 ◽  
Vol 409-410 ◽  
pp. 1392-1397
Author(s):  
Yun Long Tan ◽  
Hong Fei Jia
Keyword(s):  
Fuzzy Clustering ◽  
Simulation System ◽  
Test Method ◽  
Distribution Model ◽  
Type Model ◽  
Distribution Data ◽  
Type Distribution ◽  
Fuzzy Clustering Method ◽  
Vehicle Acceleration ◽  
The Impact

The driver characteristic is an important factor that affects driver behaviors, however, the existing driver behavior models little consider the influence of driver own characteristic differences on the driver behaviors. As the driver mental and physical behaviors in the process of driving are uncertainty and ambiguity, the mainline vehicles at expressway-ramp merging area are selected as research object, and the fuzzy clustering theory is introduced. In order to describe the mainline drivers characteristics accurately, the mainline vehicle acceleration, the relative speed of the current mainline vehicle to the all mainline vehicles and the lag gap of the mainline vehicle are selected to cluster by the fuzzy clustering method, and the driver type distribution model is built by K-S test method. Then, the driver type distribution data as a key parameter is incorporated into the expressway merging model, in order to represent the effect of driver characteristic on drive behavior. Finally, the microscopic traffic simulation system MTSS is taken as the simulation plat to build simulation model and validate the built mainline driver type model, the output results from the simulation system are compared with the field data, the satisfactory results indicate that the built driver type model can be used to describe the impact of driver type on driving behavior.

Major Soil Type, Soil Classification, and Soil Maps

2017 ◽  
pp. 69-101
Author(s):  
Aminaton Marto ◽  
Safiah Yusmah Mohd Yusoff
Keyword(s):  
Soil Type ◽  
Soil Classification ◽  
Soil Maps
Sign in / Sign up

Export Citation Format

Share Document