Jiaqi Wang1, Pengfei Sun1, Leilei Chen2, Jianfeng Yang3, Zhenghe Liu1, Haojie Lian1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1381-1418, 2023, DOI:10.32604/cmes.2023.023693
- 26 June 2023
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. More >
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