LoRaWAN Enabled Smart Landslide Early Detection and Alerting

Landslides are a very hazardous risk to human life, infrastructure, and the environment as well. Highly accurate early warning systems are therefore required for risk reduction. The subsequent research proposal aims to create an AI and IoT-based land-slide prediction and early warning system based on selection of various sensors like MEMS accelerometers and tilt sensors, piezoelectric sensors, soil moisture sensors, rainfall detector, and temperature sensors. These sensors will be continuously monitoring Hydrological, Metrological, Geomechanical parameters are used for monitoring probable causes of landslides. Sensor data will be analyzed by machine learning algorithms for land-slide prediction and past trends. The system will be having edge processing for real-time monitoring and early warning through alarm on mobile, siren, and warning of disaster management authorities. This project aims to develop an affordable, highly accurate, and scalable landslide prediction system by integrating sensor networks, IoT, AI and LowRaWAN. A pilot deployment in landslide-prone regions will assess its field performance and reliability. By delivering real-time, dependable landslide alerts, the system will enhance disaster preparedness, minimize casualties, and streamline evacuation planning.