Journal ArticleDOI
Support vector machine applied to settlement of shallow foundations on cohesionless soils
TLDR
In this article, a support vector machine (SVM) was used to predict the settlement of shallow foundations on cohesionless soil, and a thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement.About:
This article is published in Computers and Geotechnics.The article was published on 2008-05-01. It has received 183 citations till now. The article focuses on the topics: Settlement (structural) & Shallow foundation.read more
Citations
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Journal ArticleDOI
Machine learning in geosciences and remote sensing
TL;DR: The role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted and unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm.
Journal ArticleDOI
Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS
TL;DR: In this paper, an ensemble method, which demonstrated efficiency in GIS-based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia, which is used to estimate...
Journal ArticleDOI
Determination of earth pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines approach
TL;DR: In this article, a multivariate adaptive regression splines (MARS) approach was used to establish relationships between the maximum surface settlement and the major influencing factors, including the operation parameters, the cover depth and the ground conditions.
Journal ArticleDOI
Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling
TL;DR: P predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting (XGBoost), artificial neural network, support vector machine, and multivariate adaptive regression spline, demonstrating acceptable accuracy of the model in predicting ground settlement.
Journal ArticleDOI
Threats of climate and land use change on future flood susceptibility
Paramita Roy,Subodh Chandra Pal,Rabin Chakrabortty,Indrajit Chowdhuri,Sadhan Malik,Biswajit Das +5 more
TL;DR: In this paper, the authors presented flood susceptible areas in Ajoy River basin using Support Vector Machine (SVM), Random Forest (RF) and Biogeography Based Optimization (BBO) model in GIS environment.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
An overview of statistical learning theory
TL;DR: How the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms are demonstrated and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems are demonstrated.