Gaussian Mixture Model Based Soil Classification Using Multiple Cone Penetration Tests

Bouayad, Djamila; Baroth, Julien; Dano, Christophe

doi:10.1088/1755-1315/696/1/012034

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Bouayad et al [15] modelled enforcement of the Gaussian mixture (GM) technique for classifier of the soil utilizing several cone penetration tests (CPT). The GM method will categorize the CPT data through indication of probability density function.…”

Section: Related Workmentioning

confidence: 99%

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Dwarf Mongoose Optimization with DL Based Soil Classification Model for Precision Agriculture

Aneetchan,

Vaithianathan

2024

RIA

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In the agricultural sector, soil quality plays a pivotal role in determining crop types. Traditional manual soil analysis can be time-consuming and reliant on a limited number of experts, often resulting in insufficient knowledge about local soil conditions. For accurate soil classification process, this study develops a Dwarf Mongoose Optimization with DL Based Soil Classification (DMODL-SC) model. The presented DMODL-SC technique majorly recognizes different kinds of soil using CV and DL models. In the presented DMODL-SC technique, bilateral filtering (BF) technique is used for noise removal process which eradicate the presence of noise exist in the soil images and enhances its quality. In addition, the presented DMODL-SC technique employs capsule network (CapsNet) model for feature extraction process. Moreover, Denoising Auto Encoder (DAE) is exploited for the identification and classification of soil. Since the manual hyperparameter tuning is a tedious process, the DMO algorithm is applied to tune the hyperparameters related to the DAE model. To demonstrate the enhanced performance of the projected DMODL-SC system, an extensive range of experiments were performed. The comparison study reported the improvised soil classification performance of the DMODL-SC technique over other approaches with maximum accuracy of 95.92%.

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Section: Related Workmentioning

confidence: 99%

“…𝑛 𝑓𝑖𝑡 𝑖 (15) Each mongoose sleep from the initial sleeping mound that is set ∅ . The DMO exploits to generate a candidate food location.…”

Section: 𝛼 = 𝑓𝑖𝑡 𝑖 𝛴 𝑖=1mentioning

confidence: 99%

Dwarf Mongoose Optimization with DL Based Soil Classification Model for Precision Agriculture

Aneetchan,

Vaithianathan

2024

RIA

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“…This was achieved with the use of reliable mechanical properties from PMT, and the groups were assigned probabilistic using a clustering algorithm. Clustering analyses have been applied by many researchers to classify soils, including classifications based on PMT (Bhargavi et al, 2010;Bhattacharya & Solomatine, 2006;Bilski, 2015;Bouayad et al, 2021;Coelho et al, 2021;Goktepe et al, 2005;Han et al, 2011;Hegazy & Mayne, 2002;Hot & Popović-Bugarin, 2016;Kloosterman & Lavkulich, 1973;Liao & Mayne, 2007;Maione et al, 2022;Monnet, 2008;Monnet & Broucke, 2012;Rabarijoely et al, 2007;Zhao & Wang, 2020).…”

Section: Prior and Current Researchmentioning

confidence: 99%

Clustering and Characterization of Toronto Soils Using Pressuremeter Tests

Siscate Bohrer

2024

Preprint

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<p>This research aims to cluster and characterize Toronto soils based on pressuremeter tests (PMT) by applying machine learning algorithms. More than 400 PMT results were collected from a transit project in Toronto, Canada. Gaussian Mixture clustering was applied to cluster Toronto soils into four main groups with distinct mechanical behaviour: glacial tills I and II, cohesive, and cohesionless deposits. A material index based on the shape of the PMT curve was proposed to enhance the separation between cohesive and cohesionless soils, and PMT soil behaviour charts of material index (M IDvs. Menard modulus (E ) Mere proposed. The isocurves of the coefficient Œ± can be used to estimate soil's elastic moduli by correcting the pre-boring PMT disturbance. Predictive equations for EMandM weID proposed enabling the application of the PMT charts in practice. The application of French PMT charts was investigated, and comparisons between Toronto and UK tills were performed.</p>

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“…Bouayad et al [22] presented a system for soil classification utilizing several cone penetration tests based on the Gaussian mixture (GM) method (CPT). In contrast to hard clustering, the GM model classifies CPT data by treating the probability density function of the measured variables as a mixture of multivariate normal distributions.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Classification Algorithms for Analyzing Soil Data

Taher

Abdulazeez

Zebari

2021

AJRCoS

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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.

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Gaussian Mixture Model Based Soil Classification Using Multiple Cone Penetration Tests

Cited by 8 publications

References 12 publications

Dwarf Mongoose Optimization with DL Based Soil Classification Model for Precision Agriculture

Dwarf Mongoose Optimization with DL Based Soil Classification Model for Precision Agriculture

Clustering and Characterization of Toronto Soils Using Pressuremeter Tests

Data Mining Classification Algorithms for Analyzing Soil Data

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