Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining-Reference-Cited by-同舟云学术

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Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining

Published:2024-09-09 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Wu Zhengyu,Chen Ying,Luo Dayou

Funder

National Natural Science Foundation of China

Open Project of Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education

Natural Fund project of Fujian Province Science and Technology Department

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-71928-9.pdf

Reference55 articles.

1. Dai, B. & Chen, Y. A novel approach for predicting the height of the water-flow fracture zone in undersea safety mining. Remote Sens. 12, 358 (2020).

2. Kang, P., Zhaopeng, L., Quanle, Z., Zhenyu, Z. & Jiaqi, Z. Static and dynamic mechanical properties of granite from various burial depths. Rock Mech. Rock Eng. 52, 3545–3566 (2019).

3. Li, Q.-Y., Chen, G., Luo, D.-Y., Ma, H.-P. & Liu, Y. An experimental study of a novel liquid carbon dioxide rock-breaking technology. Int. J. Rock Mech. Min. Sci. 128, 104244 (2020).

4. Wang, Z., Li, W., Wang, Q., Hu, Y. & Du, J. Monitoring the dynamic response of the overlying rock–soil composite structure to underground mining using BOTDR and FBG sensing technologies. Rock Mech. Rock Eng. 54, 5095–5116 (2021).

5. Zhengzheng, C., Xiangqian, Y., Zhenhua, L. & Feng, D. Evolution mechanism of water-conducting fractures in overburden under the influence of water-rich fault in underground coal mining. Sci. Rep. 14, 5081 (2024).

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