TY  - EJOU
AU  - Wang, Jiaqi 
AU  - Sun, Pengfei 
AU  - Chen, Leilei 
AU  - Yang, Jianfeng 
AU  - Liu, Zhenghe 
AU  - Lian, Haojie 

TI  - Recent Advances of Deep Learning in Geological Hazard Forecasting
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2023
VL  - 137
IS  - 2
SN  - 1526-1506

AB  - 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. 
KW  - Geological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic

DO  - 10.32604/cmes.2023.023693