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Research on geological hazard identification based on deep learning

Authors :
Tao Cheng
Cheng Zhu
Source :
AIP Conference Proceedings.
Publication Year :
2018
Publisher :
Author(s), 2018.

Abstract

Geological hazards such as landslides, debris flows and collapses are potential hazards affecting the safety of nearby roads and people. Land and Resources Bureau and other relevant departments to undertake the responsibility of prevention and control of geological disasters, an important body, how to deal with the characteristics of sudden geological disasters in the region, according to pre-established emergency measures quickly and accurately survey, is an important issue to be solved. Based on the analysis of the types and effects of typical geological disasters, this paper studies the relevant methods of identifying typical geological disasters through artificial neural networks, and proposes and designs intelligent geological survey methods and systems based on deep learning to provide relevant departments such as Land and Resources Bureau Related Mountain Geological Survey and Information Support.Geological hazards such as landslides, debris flows and collapses are potential hazards affecting the safety of nearby roads and people. Land and Resources Bureau and other relevant departments to undertake the responsibility of prevention and control of geological disasters, an important body, how to deal with the characteristics of sudden geological disasters in the region, according to pre-established emergency measures quickly and accurately survey, is an important issue to be solved. Based on the analysis of the types and effects of typical geological disasters, this paper studies the relevant methods of identifying typical geological disasters through artificial neural networks, and proposes and designs intelligent geological survey methods and systems based on deep learning to provide relevant departments such as Land and Resources Bureau Related Mountain Geological Survey and Information Support.

Details

ISSN :
0094243X
Database :
OpenAIRE
Journal :
AIP Conference Proceedings
Accession number :
edsair.doi...........83c28a1fef5bfda13d7b957e4b843494
Full Text :
https://doi.org/10.1063/1.5039101