Statistics and Data Analysis in Geology

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

https://davidlary.info/wp-content/uploads/2015/09/1-s2.0-S1674987115000821-main.pdf

Machine learning in geosciences and remote sensing

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Computer Aided Civil and Infrastructure Engineering

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for
determination of fs of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of fs of driven piles in clay.

Multivariate adaptive regression spline applied tofriction capacity of driven piles in clay

Geotechnical engineers use in situ tests for site characterization. In this paper, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as standard penetration test (SPT), cone penetration tests(CPT) and multi channel analysis of surface wave (MASW) techniques. For this purpose a typical site was selected wherein a man made homogeneous embankment and as well natural ground has been met. For this typical site, in situ tests (SPT, CPT and MASW) have been carried out in different ground conditions and the obtained test results are compared. Three CPT continuous test profiles, fifty-four SPT tests and nine MASW test profiles with depth have been carried out for the selected site covering both homogeneous embankment and natural ground. Relationships have been developed between V s , ( N 1 )60 and q c values for this specific site.

Correlation between SPT, CPT and MASW

This article employs Least Square Support Vector Machine (LSSVM) for determination of Compression Index (Cc) of marine clay in east coast of Korea. This study uses LSSVM as a regression tool. In LSSVM, the regression equation is obtained as the solution to a linear system instead of a quadratic programming (QP) problem. The input parameters of LSSVM are natural water content (ωn), liquid limit (ω1), initial void ratio (e0), and plasticity index (PI). Equations have been also developed for the prediction of Cc of marine clay. The comparison between the developed LSSVM and the regression models shows that the developed LSSVM models perform better than the regression models. This article shows that the developed LSSVM can be used to predict Cc of marine clay in east coast of Korea.

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Determination of Compression Index For Marine Clay: A Least Square Support Vector Machine Approach

Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques

This article adopts three statistical learning algorithms: support vector machine (SVM), lease square support vector machine (LSSVM), and relevance vector machine (RVM), for predicting compressive strength (f c ) of concrete. Fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA), and age of samples (AS) are used as input parameters of SVM, LSSVM and RVM. The output of SVM, LSSVM and RVM is f c . This article gives equations for prediction of f c of concrete. A comparative study has been carried out between the developed SVM, LSSVM, RVM and Artificial Neural Network (ANN). This article shows that the developed SVM, LSSVM and RVM models are practical tools for the prediction of f c of concrete.

/pdf/determination-of-compressive-strength-of-concrete-by-3qxpeu4g9a.pdf

Pijush Samui

Papers

Multivariate adaptive regression spline applied tofriction capacity of driven piles in clay

Correlation between SPT, CPT and MASW

Determination of Compression Index For Marine Clay: A Least Square Support Vector Machine Approach

Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques

Determination of Compressive Strength of Concrete by Statistical Learning Algorithms