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

Contamination of well with nitrate-N (NO3-N) posses various threats to human health. This problem becomes even more critical when these wells serve as source of drinking water as in the case of many rural parts of USA. This article employs Relevance Vector Machine (RVM) for determination of non-contaminated and contaminated well with nitrate-N (NO3-N) in Polk County, Florida (USA). This research will provide a regional scale integrated GIS-based modeling approach to predict NO3-N contamination of ground water in a cost effective way. This approach also allows for higher true positive results (TPR) with fewer variables when data are imprecise and full of uncertainty which is common with available regional scale data). RVM technique is a Bayesian extension of the Support Vector Machine (SVM). Here, the RVM has been used as a classification tool. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) has been used to develop the RVM model. An equation has been also presented from the developed RVM model. The developed RVM has been compared with the Artificial Neural Network (ANN) and SVM models. This study shows that the developed RVM produces promising result for prediction of non-contaminated and contaminated well with N. The model is important because its real world applications enable water managers to more effectively manage contaminant levels within specific watersheds.

Determination of contaminatated wells to no3-n: a novel vulnerability assessment tool

Application of deep learning approaches to predict monthly stream flows

Abstract In geotechnical engineering, transformation models are often used as first estimates of parameters and to verify the order of magnitude of field and laboratory tests, which reliability might be constrained by many uncertainties. The undrained shear strength has been for long of particular interest for such models. The traditional transformation models for undrained shear strength are often rather simple. Still, the geotechnical community does not seem to have agreed upon which models to use. In particular, the question of including index properties to the models seems to be open. In the paper, the performance of traditional transformation models is compared to that of machine learning (ML)-based models. In addition, the influence of data coherence is studied by using two datasets of different quality. The ML-based transformation models proved to perform better than traditional ones for both datasets. Clearly, most dominant variables in the transformation model are the preconsolidation pressure and the effective vertical stress. Although including additional variable often may well improve the performance of the training set, the prediction of the testing sets generally tends to worsen, indicating overtraining. The risks for overtraining increase with incoherent data. 

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Performance of traditional and machine learning-based transformation models for undrained shear strength

In the present paper, multivariate adaptive regression splines (MARS) method is used to carry out reliability analysis of Durgawati earthen dam. The steady and transient state seepage is considered while calculating reliability index \(\left( \beta \right)\). The earthen dam is an important water retaining structure and serves the catchment area of Durgawati River in Bihar, India. The factor of safety (FOS) of the earthen dam against slope failure under steady and transient state is performed using GeoStudio-07 software. In order to account for the variability of material parameters over the body of the dam, a data set of material parameters [i.e., cohesion (\(c\)), angle of internal friction \(\left( \varphi \right)\) and soil unit weight (\(\gamma\))] are prepared, and MARS is used to calculate reliability index \(\left( \beta \right)\) for different realizations over the dam geometry. The reliability index \(\left( \beta \right)\) thus estimated shows that the dam is stable and safe from slope failure point of view under different seepage loadings.

Multivariate Adaptive Regression Spline Based Reliability Analysis of Stability of Durgawati Earthen Dam

Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset.

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Pijush Samui

Papers

Determination of contaminatated wells to no3-n: a novel vulnerability assessment tool

Application of deep learning approaches to predict monthly stream flows

Performance of traditional and machine learning-based transformation models for undrained shear strength

Multivariate Adaptive Regression Spline Based Reliability Analysis of Stability of Durgawati Earthen Dam

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features