Smart IoT System for Landslide Prediction and Monitoring

Landslides pose significant threats to both human life and infrastructure, necessitating early detection and continuous monitoring for effective disaster prevention and risk mitigation. Traditional monitoring methods, such as on-site inspections to assess terrain and soil conditions, are labor – intensive, time-consuming, and often impractical in remote or hazardous locations. This study presents an automated landslide monitoring and prediction system leveraging the Internet of Things (IoT) and various environmental sensors. The proposed system integrates soil moisture, vibration, and rainfall sensors with an ESP8266 microcontroller to collect real-time environmental data. The data is transmitted to a cloud-based platform where advanced analytics evaluate the input to detect early signs of landslide risk. The system triggers alerts when predefined thresholds are exceeded, enabling proactive response and timely evacuation if necessary. The entire solution is designed for continuous operation and scalability, ensuring reliable monitoring of landslide-prone areas. Experimental implementation demonstrates high precision and responsiveness in detecting potential landslides, highlighting the system’s potential to enhance disaster preparedness. This research contributes to the development of intelligent, cost-effective, and automated environmental monitoring systems, reducing reliance on manual inspection and improving early warning capabilities.