AI-Powered Soil Stability Assessment and Landslide Forecasting with Multi-Sensor Networks, Edge Computing, and Jetson Nano Integration

Landslides pose a severe threat to mountainous regions, often resulting in catastrophic damage and loss of life. Existing prediction methods rely on limited data sources and infrequent monitoring, leading to delays in early warnings. Previous technologies have explored individual sensor applications, such as Ground-Penetrating Radar (GPR) and seismic sensors, but lack a comprehensive multi-sensor approach with real-time processing. Notable cases, like the Wayanad landslides in Kerala, India, highlight the urgent need for advanced predictive systems. This study proposes an AI-driven landslide prediction system using a network of 40 sensor-equipped slave kits. Each kit integrates GPR, seismic, Electromagnetic Induction (EMI), ultrasonic, LiDAR, capacitance probes, gravimeters, Electrical Resistivity Tomography (ERT), and magnetometers onto a Teensy 4.1 microcontroller with an ESP8266 for wireless data transmission. Data is processed at a central master unit featuring a Jetson Nano module, employing the ALEXNET+BILSTM model for real-time analysis of soil stability and landslide risk. By leveraging edge computing, this system enables rapid decision-making and early warnings, addressing the limitations of existing methods. Its scalable, cost-effective architecture enhances environmental monitoring, offering a robust solution for landslide-prone communities and significantly improving disaster preparedness.