TY  - JOUR
PY  - 2022//
TI  - Exploration of landslide detection model using deep learning
JO  - International journal of engineering technology and management sciences
A1  - Rajeswari, R.
A1  - Surendra, Borra
A1  - Dhilipkumar, D.
A1  - Krishnan, S. Gogula
SP  - 80
EP  - 87
VL  - 6
IS  - 5
N2  - People who work in mining locations put their lives on the line for valuable resources; landslides and the emission of hazardous gases are the leading causes of worker fatality. In general, underground mining is far more perilous than surface mining. The landslide occurs in a matter of seconds, and although warning surface mine employees is simple, informing underground mining personnel is complicated and has a low likelihood of escape. The proposed solution for our project is to use deep learning technology to monitor and anticipate landslides. It does an amount analysis of the mining zone, which is backed by IoT sensors that monitor the landslide. A trained model is developed and fed into the computer system. By predictive analysis, the system issues an alarm<p />  <p>Language: en</p>
LA  - en
SN  - 2581-4621
UR  - http://dx.doi.org/10.46647/ijetms.2022.v06i05.011
ID  - ref1
ER  -