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Recent Advances of Deep Learning in Geological Hazard Forecasting

Jiaqi Wang1, Pengfei Sun1, Leilei Chen2, Jianfeng Yang3, Zhenghe Liu1, Haojie Lian1,*

1 Key Laboratory of in-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan, 030024, China
2 School of Architectural and Civil Engineering, Huanghuai University, Zhumadian, 463003, China
3 School of Energy Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

* Corresponding Author: Haojie Lian. Email: email

Computer Modeling in Engineering & Sciences 2023, 137(2), 1381-1418. https://doi.org/10.32604/cmes.2023.023693

Abstract

Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards.

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APA Style
Wang, J., Sun, P., Chen, L., Yang, J., Liu, Z. et al. (2023). Recent advances of deep learning in geological hazard forecasting. Computer Modeling in Engineering & Sciences, 137(2), 1381-1418. https://doi.org/10.32604/cmes.2023.023693
Vancouver Style
Wang J, Sun P, Chen L, Yang J, Liu Z, Lian H. Recent advances of deep learning in geological hazard forecasting. Comput Model Eng Sci. 2023;137(2):1381-1418 https://doi.org/10.32604/cmes.2023.023693
IEEE Style
J. Wang, P. Sun, L. Chen, J. Yang, Z. Liu, and H. Lian "Recent Advances of Deep Learning in Geological Hazard Forecasting," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1381-1418. 2023. https://doi.org/10.32604/cmes.2023.023693



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