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AI-Based Landslide Early Warning System

Team Details

Team Name: Red Bull
Team Leader: Rishikesh R


Problem Statement

Sudden landslides caused by rainfall-induced soil weakening and ground movement pose serious safety risks in mines and hilly regions. Existing monitoring systems are often expensive, reactive, or dependent on continuous internet connectivity, making them unsuitable for remote mining environments.


Project Overview

This project proposes a low-cost, IoT-based Landslide Early Warning System designed to detect early warning signs of landslides and ensure immediate worker safety.

The system continuously monitors:

  • 🌧️ Rainfall
  • 🌱 Soil moisture levels
  • 🌍 Ground movement

Using an ESP32 edge device, the system evaluates landslide risk locally and classifies it as SAFE or DANGER.


Key Features

  • Real-time environmental monitoring
  • Local risk detection using rule-based intelligence
  • 🚨 Instant siren activation for worker evacuation
  • 📶 Offline alert capability (does not depend on internet for safety)
  • ☁️ Cloud-based monitoring dashboard for management
  • 📱 SMS/notification support for supervisors

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