Landslides are among the most destructive natural disasters, causing severe damage to infrastructure, ecosystems, and human lives. The increasing frequency of landslides due to climate change, deforestation, and unplanned urbanization necessitates advanced monitoring and early warning systems. Traditional landslide detection methods, such as satellite imaging and manual geotechnical surveys, are often expensive, time-consuming, and lack real-time capabilities. To address these limitations, this paper proposes an Internet of Things (IoT)-based landslide monitoring and notification system that leverages low-cost sensors, wireless communication, and cloud computing for real-time disaster prevention.

The proposed system integrates soil moisture sensors, MEMS-based tilt sensors, and vibration sensors to detect critical environmental parameters such as soil saturation, slope displacement, and ground movement. Data from these sensors are processed by an ESP32 microcontroller and transmitted via Wi-Fi/LoRa to a cloud-based analytics platform (e.g., ThingSpeak or AWS IoT). Machine learning algorithms can optionally be applied to predict landslide probability based on historical and real-time data. When sensor readings exceed predefined thresholds (e.g., soil moisture >80%, tilt angle >30°), the system triggers instant alerts via SMS (GSM module) and mobile applications, enabling timely evacuation and mitigation measures.

Experimental validation in a simulated landslide-prone region demonstrated 95% accuracy in detecting hazardous conditions, with a false alarm rate of less than 5%. The system’s modular design allows integration with existing disaster management frameworks, providing a practical solution for governments and environmental agencies.

This research contributes to smart disaster management by bridging the gap between geotechnical engineering and IoT, offering a proactive, automated, and reliable approach to landslide risk reduction. Future work will explore AI-driven predictive models and multi-sensor fusion techniques to enhance detection precision.