LANDSLIDE PREDICTION WITH AN AID OF IOT

Landslides represent a significant geohazard in mountainous and hilly topographies, often resulting in devastating socio-economic impacts and loss of life. Traditional monitoring techniques, which rely on manual observations or static threshold-based triggers, frequently lack the real-time responsiveness and predictive accuracy required for effective early warnings. This research proposes an Integrated IoT and Deep Learning framework for proactive landslide monitoring. The system utilizes a multi-sensor array—including soil moisture, rainfall intensity, and geomechanical vibration sensors—interfaced with an Arduino-based telemetry node. The core of the system is a Long Short-Term Memory (LSTM) neural network, designed to identify non-linear temporal dependencies and subtle environmental precursors of slope failure. A real-time, interactive Streamlit dashboard was developed to provide stakeholders with high-fidelity geospatial intelligence, including 3D topographic mapping and dynamic threat-vector analysis. When risk levels exceed critical safety margins, the system triggers an immediate multi-channel alert sequence. The findings demonstrate that this low-cost, scalable architecture provides a robust solution for geohazard monitoring in remote regions. By synthesizing real-time IoT telemetry with predictive AI models, this research contributes a high-accuracy and accessible platform for modern disaster mitigation and resilient community planning.