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

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

https://dergipark.org.tr/en/download/article-file/2571841

Application of Soft Computing Techniques in River Flow Modeling

This article examines the performance of Minimax Probability Machine Regression (MPMR) for prediction of settlement(s) of shallow foundation on cohesionless soil. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. Width of footing (B), net applied pressure (q), average Standard Penetration Test (SPT) blow count (N), length (L), and embedment depth (Df) have been adopted as inputs of the MPMR. A sensitivity analysis has been carried out to determine the effect of each input. The results of MPMR have been compared with the Artificial Neural Network (ANN).

Use of Minimax Probability Machine Regression for Modelling of Settlement of Shallow Foundations on Cohesionless Soil

In this study, various machine learning algorithms, including the minimax probability machine regression (MPMR), functional network (FN), convolutional neural network (CNN), recurrent neural network (RNN), and group method of data handling (GMDH) models, are proposed for the estimation of the seismic bearing capacity factor (Nc) of strip footings on sloping ground under seismic events. To train and test the proposed machine learning model, a total of 1296 samples were numerically obtained by performing a lower-bound (LB) and upper-bound (UB) finite element limit analysis (FELA) to evaluate the seismic bearing capacity factor (Nc) of strip footings. Sensitivity analysis was performed on all dimensionless input parameters (i.e., slope inclination (β); normalized depth (D/B); normalized distance (L/B); normalized slope height (H/B); the strength ratio (cu/γB); and the horizontal seismic acceleration (kh)) to determine the influence on the dimensionless output parameters (i.e., the seismic bearing capacity factor (Nc)). To assess the performance of the proposed models, various performance parameters—namely the coefficient of determination (R2), variance account factor (VAF), performance index (PI), Willmott’s index of agreement (WI), the mean absolute error (MAE), the weighted mean absolute percentage error (WMAPE), the mean bias error (MBE), and the root-mean-square error (RMSE)—were calculated. The predictive performance of all proposed models for a bearing capacity factor (Nc) prediction was compared by using the testing dataset, and it was found that the MPMR model achieved the highest R2 values of 1.000 and 0.957 and the lowest RMSE values of 0.000 and 0.038 in both the training and testing phases, respectively. The parametric analyses, rank analyses, REC curves, and the AIC showed that the proposed models were quite effective and reliable for the estimation of the bearing capacity factor (Nc).

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Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes

The determination of wt% alumina (wa) and pore volume fraction (pv) in alumina-based bioceramics is important in ceramic engineering. This article adopts support vector machine (SVM) and relevance vector machine (RVM) for prediction of wa and pv based on SiC. SVM is firmly based on theory of statistical learning. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The developed SVM and RVM give equations for prediction of wa and pv. This article gives robust models based on SVM and RVM for prediction of wa and pv.

Pijush Samui

Papers

Application of Soft Computing Techniques in River Flow Modeling

Use of Minimax Probability Machine Regression for Modelling of Settlement of Shallow Foundations on Cohesionless Soil

Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes

Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics

Slope stability analysis of heavy-haul freight corridor using novel machine learning approach