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.

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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

The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance. RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models give probabilistic output. A comparative study has been also done between developed two models and artificial neural network model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data.

Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment. For this purpose, a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM. Initially, the main dataset was utilised to construct and validate four conventional soft computing (CSC) models, i.e. minimax probability machine regression, relevance vector machine, extreme learning machine, and functional network. Consequently, the estimated outputs of CSC models were united and trained using an artificial neural network (ANN) to construct a hybrid ensemble model (HENSM). The outcomes of the proposed HENSM are superior to other CSC models employed in this study. Based on the experimental results (training RMSE = 0.0283 and testing RMSE = 0.0418), the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This article investigates the feasibility of multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for the prediction of over consolidation ratio (OCR) of clay deposits based on Piezocone Penetration Tests (PCPT) data MARS uses piece-wise linear segments to describe the non-linear relationships between input and output variables LSSVM is firmly based on the theory of statistical learning, and uses regression technique The input parameters of the models are corrected cone resistance (q t ), vertical total stress (σv), hydrostatic pore pressure (u 0), pore pressure at the cone tip (u 1), and the pore pressure just above the cone base (u 2) The developed LSSVM model gives error bar of predicted OCR Equations have also been developed for prediction of OCR The performance of MARS and LSSVM models has been compared with the traditional methods for OCR prediction As the results reveal, the proposed MARS and LSSVM models are robust models for determination of OCR

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

This article examines the potential of support vector machine (SVM) in stability status prediction of slope Support vector machine achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model rather than the minimizing the error on the training data only This study uses SVM as a classification tool In this article, the input data for slope stability prediction consist of values of geotechnical and geometric properties of slope The accuracy of the SVM is 100% for this problem The developed SVM gives also an equation for prediction of status of slope This study shows that SVM has the potential to be a useful and practical tool for prediction of slope stability

Support vector classifier analysis of slope

In the recent year, extensive researches have been done on fly ash-based geopolymer concrete for its similar properties like Portland cement as well as its environmental sustainability. However, it is difficult to provide a consistent method for geopolymer mix design because of the complexity and uncertainty of its design parameters, such as the alkaline solution concentration, mole ratio, and liquid to fly ash mass ratio. These mix-design parameters, along with the curing time and temperature ominously affect the most significant properties of the geopolymer concrete, i.e., compressive strength. To overcome these difficulties, the paper aims to provide a simple mix-design tool using artificial intelligence (AI) models. Three well-established and efficient AI techniques namely, genetic programming, relevance vector machine, and Gaussian process regression are used. Based on the performance of the developed models, it is understood that all the models have the capability
to deliver higher prediction accuracies in the range of 0.9362 to 0.9905 (based on R2 value). Among the employed models, RVM outperformed the other model with R2=0.9905 and RMSE=0.0218. Theodore, the developed RVM model is very potential to be a new alternative to assist engineers to save time and expenditure on account of the trial-and-error process in finding the correct design mix proportions.

Pijush Samui

Papers

Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

Support vector classifier analysis of slope

Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete