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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Wan, J., Sun, P., Chen, L., Yang, J., Liu, Z., & Lian, H. (2023). Recent Advances of Deep Learning in Geological Hazard Forecasting. CMES - Computer Modeling in Engineering and Sciences. Tech Science Press. https://doi.org/10.32604/cmes.2023.023693
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