Scalable Real-Time Landslide Forecasting System Leveraging IoT Sensors, AI, and Cloud-Edge Integration

Landslides are among the most destructive natural hazards, especially in mountainous areas, and inflict massive damage on infrastructure, the environment, and human life. Historical-based and non-real-time landslide forecasting systems are traditional and thus response is delayed and vulnerability is increased. In this article, a real-time IoT-based landslide forecasting and evacuation warning system is proposed that incorporates sensor networks, machine learning, cloud computing, and edge computing. The system uses environmental sensors such as soil moisture sensors, geotechnical strain sensors, accelerometers, and rain gauges to identify early warning signs of slope instability. Sensor data are sent to a cloud platform, where random forests, support vector machines, and deep neural networks are used for precise risk assessment. Real-time warnings are sent through mobile apps, geospatial dashboards, and IoT-based sirens for issuing timely warnings to people and authorities. The system also includes automated emergency protocol activation to increase evacuation and rescue operations. Edge computing provides support for local data processing, hence ensuring low-latency response for areas with poor connectivity. Future improvements include AI-based predictive modeling, blockchain-based data management, satellite image integration [11], and drone-based monitoring of terrain. The system increases disaster preparedness and response significantly and is a scalable, proactive solution to landslide risk reduction.