A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01043-2.pdf
Reference20 articles.
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3. Gao BY, Wang RR, Lin CJ (2020) TBM penetration rate prediction based on the long short-term memory neural network. Undergr Space 6(6):718–731
4. He P, Wu WJ (2023) Levy flight-improved grey wolf optimizer algorithm-based support vector regression model for dam deformation prediction. Front Earth Sci 11:1122937. https://doi.org/10.3389/FEART.2023.1122937
5. Henry CH, Nick JMA, Martin NG, John S (2004) Microwave paleointensities from dyke chilled margins: a way to obtain long-term variations in geodynamo intensity for the last three billion years. Phys Earth Planet Inter 147(2):183–195. https://doi.org/10.1016/j.pepi.2004.03.013
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