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

The nature of soil varies horizontally as well as vertically, owing to the process of the formation of soil. Thus, ensuring the safe design of geotechnical structures has been a major challenge. In shallow foundations, conducting field tests is expensive and time-consuming and often conducted on significantly scaled-down models. Empirical models, too, have been found to be the least reliable in the literature. The study proposes AI-based techniques to predict the bearing capacity of a shallow foundation, simulated using the datasets obtained in experiments conducted in different laboratories in the literature. The results of the ELM-EO and ELM-PSO hybrid models are compared with that of the ELM and MARS models. The performance of the models is analyzed and compared with each other using various performance parameters. The models are graded to each other using rank analysis and the visual interpretations are provided using error matrices and REC curves. ELM-EO is concluded to be the best performing model (R2 and RMSE equal to 0.995 and 0.01, respectively, in the testing phase), closely followed by ELM-PSO, MARS, and ELM. The performance of MARS is better than ELM (R2 equals 0.97 and 0.5, respectively, in the testing phase); however, hybridization greatly enhances the performance of the ELM and the hybrid models perform better than MARS. The paper concludes that AI-based models are robust and hybridization of regression models with optimization techniques should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with friction angle being the highest.

/pdf/hybrid-elm-and-mars-based-prediction-model-for-bearing-16uom2vo.pdf

Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation

Asphaltene precipitation is the reason behind some of the most destructive issues in the oil industry such as wettability alteration, formation plugging, and reduction of the relative permeability....

Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR

This study adopts Multivariate Adaptive Regression Splines (MARS) for prediction of Evaporation Losses (E) in reservoirs. MARS is a technique to estimate general functions of highdimensional arguments given sparse data. The input variables of MARS model are Mean air temperature, Average wind speed, Sunshine hours, and Mean relative humidity. An equation has been developed for prediction of E based on the developed MARS. The results of MARS are compared with the Artificial Neural Network (ANN) model. This study shows that the developed MARS is a robust model for prediction of E in reservoirs.

/pdf/application-of-multivariate-adaptive-regression-splines-to-31tpeiq78o.pdf

Application of Multivariate Adaptive Regression Splines to Evaporation Losses in Reservoirs

In this research, the machine learning techniques such as, minimax probability machine regression (MPMR), relevance vector machine (RVM), genetic programming (GP), emotional neural network (ENN) and extreme learning machine (ELM) were utilized in the event of forecasting the 28 days compressive strength of fly ash concrete. In the present examination, exploratory database enveloping appropriate information recovered from a few past investigations has been made and used to prepare and approve the abovementioned MPMR, RVM, GP, ENN and ELM models. The database consists of cement, fly ash, coarse aggregate, fine aggregate, water, and water-binder ratio as the inputs whereas compressive strength of the concrete for 28 days is the output. The capability of the described models can be assessed by distinctive statistical parameters. The results from the mentioned models have been compared and decided that the MPMR model (R = 0.992) could be occupied as a decisive and authoritative data astute approach for forecasting the compressive strength of concrete which was fusion with fly ash as the admixture, thus preserving the tedious laboratory works. The accuracy of the adopted techniques was justified by comparing the distinct statistical parameters, distribution figures, and Taylor diagrams.

Compressive strength prediction of fly ash concrete by using machine learning techniques

https://www.techscience.com/CMES/v128n3/44007/pdf

Pijush Samui

Papers

Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation

Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR

Application of Multivariate Adaptive Regression Splines to Evaporation Losses in Reservoirs

Compressive strength prediction of fly ash concrete by using machine learning techniques

Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer