Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential-Reference-Cited by-同舟云学术

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Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential

Published:2022-02-19 Issue:7 Volume:55 Page:5673-5705
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Zhou Jian^ORCID,Huang Shuai,Zhou Tao,Armaghani Danial Jahed,Qiu Yingui

Funder

Innovation-Driven Project of Central South University

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10140-5.pdf

Reference111 articles.

1. Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Evaluating seismic soil liquefaction potential using bayesian belief network and C4.5 decision tree approaches. Appl Sci-Basel 9(20):4226

2. Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Appl Sci-Basel 9(2):233

3. Ahmad M, Tang X-W, Qiu J-N, Ahmad F (2021a) Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks. Front Struct Civ Eng 15(1):80–98

4. Ahmad M, Tang X-W, Qiu J-N, Ahmad F, Gu W-J (2020a) A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: exploration from historical data. Front Struct Civ Eng 14(6):1476–1491

5. Ahmad M, Tang X-W, Qiu J-N, Ahmad F, Gu W-J (2021b) Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Front Struct Civ Eng 15(2):490–505

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