An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost-Reference-Cited by-同舟云学术

An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost

Published:2022-10-08 Issue:4 Volume:35 Page:3173-3190
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Demir Selçuk^ORCID,Sahin Emrehan Kutlug^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07856-4.pdf

Reference90 articles.

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2. Ishihara K, Koga Y (1981) Case studies of liquefaction in the 1964 Niigata earthquake. Soils Found 21(3):35–52

3. Seed HB, Idriss IM (1967) Analysis of soil liquefaction: Niigata earthquake. J Soil Mech Found Div 93(3):83–108

4. Youd T (2014) Ground failure investigations following the 1964 Alaska Earthquake. In: Proceedings of the 10th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Anchorage, AK

5. Chen L, Yuan X, Cao Z, Hou L, Sun R, Dong L, Wang W, Meng F, Chen H (2009) Liquefaction macrophenomena in the great Wenchuan earthquake. Earthq Eng Eng Vib 8(2):219–229

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