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

Very often it is difficult to develop mechanistic models for pavement geotechnical engineering problems due to its complex nature and uncertainty in material parameters. The difficulty in mechanistic analysis has forced the engineers to follows certain empirical correlations. The artificial neural network (ANN) is being as an alternate statistical method, mapping in higher-order spaces, such models can go beyond the existing univariate relationships. The applications of ANNs in pavement geotechnical engineering problems is mostly limited to constitutive modeling, with few applications on prediction of soil layer properties using Falling Weight Deflectometer (FWD), prediction of swelling potential and compute the remaining life of flexible pavements. However, ANN is considered as a ‘Black box’ system being unable to explain interrelation between inputs and output. The ANNs also have inherent drawbacks such as slow convergence speed, less generalizing performance, arriving at local minimum and over-fitting problems. Recently support vector machine (SVM) is being used due to its, better generalization as prediction error and model complexity are simultaneously minimized. SVM is based on statistical learning theory unlike ANNs (biological learning theory). The application of SVM in pavement geotechnical engineering is very much limited and to best of the knowledge such methods have not been applied to pavement geotechnical engineering. However, engineering application of numerical methods is a science as well as an art. This juxtaposition is based on the fact that even though the developed algorithms are based on scientific logic and belong to the special branch of applied mathematics, their successful application to new problems is problem oriented and is an art. As no method can be the panacea to solve all problems to the last details, their application to new areas needs critical evaluation. With above in view, an attempt has been made to develop the art of applying the above artificial intelligence techniques (ANN and SVM) to different pavement engineering problems such as prediction of compaction characteristics, permeability, swelling potential, coefficient of subgrade reaction etc. The parameters associated with the model developments are discussed in terms of guide line for its future

Application of Soft Computing Techniques to Expansive Soil Characterization

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical model in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the \'best\' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

https://www.techno-press.org/download.php?journal=ose&volume=5&num=1&ordernum=4

Prediction of uplift capacity of suction caisson in clay using extreme learning machine

This article uses Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM) for determination of Compression Index (Cc) of marine clay. MPMR is developed in a probabilistic framework. It maximizes the minimum probability of future predictions being within some bound of the true regression function. ELM is the advanced learning algorithm of single-hidden layer feed forward neural network. Natural moisture content (wn), liquid limit (LL), void ratio (e) and plasticity index (PI) have been used as inputs of MPMR and ELM. The output of MPMR and ELM is Cc. The results of MPMR and ELM have been compared with the regression models. This study gives a powerful tool based on the developed MPMR for determination of Cc of marine clay.

/pdf/minimax-probability-machine-regression-and-extreme-learning-18hej5oarz.pdf

Minimax probability machine regression and extreme learning machine applied to compression index of marine clay

This study presents a comparative analysis of conventional soft computing techniques in predicting strain of a rock sample fitted with several strain gauges in horizontal and vertical directions. For this purpose, a total of 2040 experimental test data was obtained from an experimental setup. Six conventional soft computing techniques, namely relevance vector machine, genetic programming, multivariate adaptive regression spline, minimax probability machine regression, emotional neural network, and extreme learning machine were used. These models were trained and validated with 70% and 30% observations of the main dataset, respectively. Experimental results demonstrate that most of the employed models have attained the most accurate prediction of rock strain. Overall, the result of the RVM model is significantly better than those obtained from other soft computing methods employed in this study. In the testing phase, the RVM model attained 94.0% and 99.8% accuracies (in terms of R2 value) against horizontal and vertical directions, respectively. Based on the experimental results, the RVM model has the potential to be a new alternative to assist geological/geotechnical engineers to estimate the rock strain in the design phase of civil engineering projects.

Prediction of rock strain using soft computing framework

This article adopts Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for prediction Soil Electrical Resistivity(R E ) of soil. RVM uses an improper hierarchical prior. It optimizes over hyperparameters. MPMR is a probabilistic model. Two models (MODEL I and MODEL II) have been adopted. Percentage sum of the gravel and sand size fractions (F) and Soil Thermal Resistivity(R T ) has been takes as inputs in MODEL I. MODEL II uses F,R T and saturation of soils(S) as input variables. The results of RVM and MPMR have  been compared with the Artificial Neural Network (ANN). The developed RVM and MPMR proves his ability for prediction of RE of soil.

/pdf/determination-of-electrical-resistivity-of-soil-based-on-4mtw9vn0fy.pdf

Pijush Samui

Papers

Application of Soft Computing Techniques to Expansive Soil Characterization

Prediction of uplift capacity of suction caisson in clay using extreme learning machine

Minimax probability machine regression and extreme learning machine applied to compression index of marine clay

Prediction of rock strain using soft computing framework

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR