Rainfall-induced landslides are destructive natural hazards that require timely detection and early warning to protect lives and infrastructure. This study presents the development and deployment of an IoT-based, cost-effective, real-time monitoring and early warning system that integrates surface and subsurface sensors to detect slope instability and issue timely warnings for disaster prevention. The monitoring system integrates tilt sensors, volumetric water content sensors, a MEMS-based inclinometer, a rain gauge, and a video camera, all linked to a web-based platform. Field results demonstrated that the tilt sensors effectively detected surface displacement, the volumetric water content sensors responded rapidly to rainfall infiltration, and the MEMS-based inclinometer captured subsurface displacement during rainfall events. Detailed analysis was conducted using multisource monitoring datasets collected during three specific rainfall events. An early warning method for landslides was proposed by combining the tilt rate, horizontal displacement rate derived from the MEMS-based inclinometer, and saturation index. Accordingly, critical threshold values for different warning levels were established based on tilt rate (Tr), displacement rate (Dr), and saturation index (Si). This study provides a robust strategy and guidelines for early warning systems, enabling generation of warning alarms and demonstrating immense potential to reduce the impacts of rainfall-induced shallow landslides and enhance risk management.
Mondal et al. (Sat,) studied this question.
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