• Harikrishnan Aji
• Bineeth B C
• Dona Sebastian
• Renuka C S
• Ojus Thomas Lee

A framework to an early warning to the occurrence of landslides is proposed in our project. The framework, in turn help to reduce the death toll and loss of other resources. The venture is proposed to recognize the volume of water content in soil, vibrations and developments in soil and subsequently precisely foresee the chance of a landslide event. Sensors are used to distinguish the water content, vibrations and ensuing developments of the soil. The framework comprises three layers: Sensor layer, Edge layer and Cloud layer. The sensor layer collects sensitive data using sensors and sends it to edge layer. Our system makes use of a machine learning model to improve reliability of predictions. The Edge layers receives the data and makes predictions using an ML model. The cloud layer sends the sensor data along with predictions to the user interface. Based on ML predictions it is possible to alert the people living in landslide prone areas to move to safer zones. We mainly use a buzzer to alert the people. It is also possible to make pre monsoon alerts. So it provides sufficient time gap for evacuation of people in that area.

Raspberry PI, Machine Learning, Sensors

"LandSliDer - An Early Warning System for Landslides", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.d76-d81, July-2022, Available :http://www.jetir.org/papers/JETIR2207309.pdf

"LandSliDer - An Early Warning System for Landslides", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppd76-d81, July-2022, Available at : http://www.jetir.org/papers/JETIR2207309.pdf