IoT-Based Landslide Detection and Live Warning System

Landslides are among the most devastating natural disasters worldwide, causing significant loss of life and infrastructure damage in hilly and mountainous regions. This paper presents the design and implementation of a low-cost, real-time landslide detection and early warning system based on the Internet of Things (IoT) framework. The proposed system integrates multiple heterogeneous sensors — including soil moisture sensors, vibration sensors (piezoelectric), tilt sensors, rain gauges, and temperature-humidity modules deployed across vulnerable slopes. Sensor data is collected by microcontroller nodes (ESP32/Arduino) and transmitted wirelessly via LoRaWAN or Wi-Fi to a cloud-based IoT platform (ThingSpeak/Blynk). A threshold-based machine learning algorithm processes incoming sensor data to classify risk levels (Normal, Warning, Critical) and triggers automated SMS/email alerts and visual alarms. Experimental results from a prototype deployment demonstrate a detection accuracy of 94.7%, alert latency below 2 seconds, and system uptime exceeding 98% over a 30-day evaluation period. The proposed system offers a scalable, affordable, and energy-efficient solution for real-time landslide monitoring in remote and resourceconstrained environments.