Smart Landslide Detection: A Real-Time Monitoring and Alert System for Disaster Prevention

The landslide detection system employs real-time environmental monitoring to identify potential landslides and mitigate risks in susceptible regions. The system integrates an ESP32 microcontroller with a suite of sensors, including a gyroscope for angular displacement measurement, a rain sensor for precipitation detection, and a DHT sensor for temperature and humidity acquisition. By continuously analyzing ground vibrations and slope variations, the system detects anomalous terrain shifts indicative of slope instability. The collected sensor data is processed using machine learning algorithms or predefined thresholds to assess landslide susceptibility based on historical geospatial datasets. A Python-based computational model correlates environmental parameters with past landslide occurrences, optimizing prediction accuracy. Upon detecting significant deviations in terrain stability or adverse meteorological conditions, the system triggers automated alerts via IoT-enabled communication protocols, notifying relevant authorities and at-risk populations. The predictive framework refines its accuracy over time through continuous data acquisition and model training, enhancing early warning capabilities. This proactive hazard mitigation approach ensures realtime risk assessment, facilitating timely evacuation measures and minimizing casualties and infrastructure damage in landslideprone areas.