LANDSLIDE MONITORING AND PREDICTION USING IOT AND LSTM BASED RNN

Abstract

Landslides are sudden and destructive natural disasters that can result in significant loss of life and property. Traditional prediction methods, such as geological surveys and manual monitoring, often lack the responsiveness needed for early warning. This project proposes a real-time Landslide Prediction and Monitoring System that integrates environmental sensors and machine learning algorithms to provide proactive alerts. Sensors including soil moisture, rainfall, vibration, temperature, and humidity continuously monitor terrain conditions. The collected data is processed by a machine learning model trained on historical landslide datasets to identify risk patterns and trigger alerts when critical thresholds are exceeded.

The system provides timely notifications through audible buzzers, visual indicators, and cloud-based platforms facilitating immediate response by local authorities and communities. With a modular design, the system is scalable and adaptable to different geographic regions and sensor configurations. By combining real-time monitoring with intelligent prediction, this system offers an efficient and cost- effective solution for mitigating landslide risks and enhancing disaster management.