Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference

Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference

The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learn...

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Bibliographic Details
Main Authors: Ayele Tesema Chala, Richard Ray
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
shear wave velocity prediction
geotechnical variability
uncertainty quantification
machine learning
extreme gradient boosting
generalized linear model
Online Access:https://www.mdpi.com/2076-3417/15/3/1409
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