Real-Time Data for Monitoring Rainfall-Induced Landslide using IoT System

Abstract

Rainfall-induced landslides present a significant hazard to vulnerable communities, particularly in regions prone to intense or prolonged precipitation. Conventional landslide monitoring systems often lack the capability to provide timely and accurate early warnings, limiting their effectiveness in risk mitigation. This study proposes an integrated real-time rainfall-slope monitoring framework for landslide predictions that combines Internet of Things (IoT)-based sensor networks using Arduino Nano ESP32 with the Blynk application. Three phases of work were conducted, to fulfil the objectives of study which are 1) to investigate the geotechnical properties of soil from selected slope, 2) to develop a rainfall-slope monitoring system using Arduino Nano ESP32 with Blynk application, and 3) to assess the rainfall-slope monitoring system on a lab-scale slope model under controlled conditions. The system continuously monitors slope movement and environmental parameters such as soil moisture and temperature. The system was calibrated using these parameters, with critical values such as optimum moisture content and shear strength used as thresholds. The observation of surface runoff also takes into account things such as measuring the rate of infiltration. The system has successfully measured changes in slope properties such as soil moisture levels, which increased significantly with higher volumes of artificial rainwater varied by time in seconds (1000 ml to 5000 ml under durations of 20, 40, and 60 seconds). Additionally, slope angles decreased from 45.0° to 44.5°, due to the soil saturation increment. The system effectively detected changes in each parameter within a 60-second observation window per condition. Alerts were triggered when moisture exceeded 17.5% and tilt surpassed 10 mm. The study demonstrated that calibrated thresholds based on actual soil behavior significantly improve the responsiveness and reliability of early warning systems, especially for rainfall-induced landslides.