Explainable XGBoost–SHAP Machine-Learning Model for Prediction of Ground Motion Duration in New Zealand-Reference-Cited by-同舟云学术

Explainable XGBoost–SHAP Machine-Learning Model for Prediction of Ground Motion Duration in New Zealand

Published:2024-05 Issue:2 Volume:25 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Somala Surendra Nadh¹,Chanda Sarit²^ORCID,AlHamaydeh Mohammad³^ORCID,Mangalathu Sujith⁴^ORCID

Affiliation:

1. Associate Professor, Dept. of Civil Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana 502285, India.

2. Research Scholar, Dept. of Civil Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana 502285, India; Assistant Professor, Faculty of Science and Technology (IcfaiTech), Dept. of Civil Engineering, ICFAI Foundation for Higher Education, Hyderabad, Telangana 501203, India. ORCID: .

3. Professor, Dept. of Civil Engineering, College of Engineering, American Univ. of Sharjah, P.O. Box 26666, Sharjah, UAE (corresponding author). ORCID: .

4. Research Scientist, Equifax, Inc., Atlanta, GA 26666. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/NHREFO.NHENG-1837

Reference61 articles.

1. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions

2. Physically Parameterized Prediction Equations for Significant Duration in Active Crustal Regions

3. Critical response evaluation of damped bilinear hysteretic SDOF model under long duration ground motion simulated by multi impulse motion

4. Arias, A. 1970. “Measure of earthquake intensity.” In Seismic design for nuclear plants, 438–483, edited by R. J. Hansen. Cambridge, MA: Massachusetts Institute of Technology Press.

5. Neural network estimation of duration of strong ground motion using Japanese earthquake records

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