A simple and flexible bootstrap-based framework to quantify epistemic uncertainty of ground motion models by light gradient boosting machine

Wen, Tianxing; He, Jianguang; Jiang, Liqiang; Du, Yanliang; Jiang, Lizhong

doi:10.1016/j.asoc.2023.111195

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2023.111195

|View full text |Cite

A simple and flexible bootstrap-based framework to quantify epistemic uncertainty of ground motion models by light gradient boosting machine

Tianxing Wen,

Jianguang He,

Liqiang Jiang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

Miao,

Liu,

Guo

et al. 2024

Knowledge-Based Systems

View full text Add to dashboard Cite

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

Miao,

Liu,

Guo

et al. 2024

Knowledge-Based Systems

View full text Add to dashboard Cite

The key to green water-preserved mining: Prediction and integration of mining rock failure height by big data fusion simulation algorithm

Li,

Yin,

Dong

et al. 2025

Process Safety and Environmental Protection

View full text Add to dashboard Cite

Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis

Kiran,

Nakkeeran,

Roy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The present research incorporates five AI methods to enhance and forecast the characteristics of building envelopes. In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning method for optimization and predicting the mechanical properties of natural fiber addition incorporated with construction and demolition waste (CDW) as replacement of Fine Aggregate in Paver blocks. In this study, factors considered were cement content, natural fine aggregate, CDW, and coconut fibre, while the resulting measure was the machinal properties of the paver blocks. Furthermore, machine learning techniques to precision the predicting machinal properties were extensively evaluated. The outcomes from both the training and testing phases demonstrated the strong predictive power of RSM, SVM, GB, ANN, and RF with a criterion used Root Mean square error (RMSE), Mean square error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). Moreover, the results demonstrated that GB and ANN provide enhanced performance in comparison to SVM and RF for determining testing factors.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A simple and flexible bootstrap-based framework to quantify epistemic uncertainty of ground motion models by light gradient boosting machine

Cited by 3 publications

References 66 publications

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization

The key to green water-preserved mining: Prediction and integration of mining rock failure height by big data fusion simulation algorithm

Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis

Contact Info

Product

Resources

About